├── README.md
├── _init_paths.py
├── config
├── h2t.config
├── j-hmdb.config
├── ucf-101.config
└── ucf-sports.config
├── data
├── embedding-example.png
├── h2t
│ ├── annotations
│ │ ├── all_videos.txt
│ │ ├── testlist.txt
│ │ └── trainlist.txt
│ └── wtv
│ │ └── h2t-wtv.npy
├── j-hmdb
│ ├── annotations
│ │ ├── all_videos.txt
│ │ ├── test-split1.txt
│ │ └── train-split1.txt
│ └── wtv
│ │ └── jhmdb-wtv.npy
├── mscoco
│ ├── mscoco-wtv.npy
│ ├── mscoco_classes.txt
│ └── mscoco_priors_new.npy
├── ucf-101
│ ├── annotations
│ │ ├── all_videos.txt
│ │ ├── test_split01.txt
│ │ └── train_split01.txt
│ └── wtv
│ │ └── ucf-101-wtv.npy
├── ucf-sports
│ ├── annotations
│ │ ├── all_videos.txt
│ │ ├── test_split.txt
│ │ └── train_split.txt
│ └── wtv
│ │ └── sports-wtv.npy
└── visualization-example.png
├── helper.py
├── local_detect.py
├── local_link.py
├── local_score.py
├── visualize
├── example
│ ├── frames
│ │ ├── 0001.jpg
│ │ ├── 0002.jpg
│ │ ├── 0003.jpg
│ │ ├── 0004.jpg
│ │ ├── 0005.jpg
│ │ ├── 0006.jpg
│ │ ├── 0007.jpg
│ │ ├── 0008.jpg
│ │ ├── 0009.jpg
│ │ ├── 0010.jpg
│ │ ├── 0011.jpg
│ │ ├── 0012.jpg
│ │ ├── 0013.jpg
│ │ ├── 0014.jpg
│ │ ├── 0015.jpg
│ │ ├── 0016.jpg
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│ │ ├── 0021.jpg
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│ │ ├── 0025.jpg
│ │ ├── 0026.jpg
│ │ ├── 0027.jpg
│ │ ├── 0028.jpg
│ │ ├── 0029.jpg
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│ │ ├── 0031.jpg
│ │ ├── 0032.jpg
│ │ ├── 0033.jpg
│ │ ├── 0034.jpg
│ │ ├── 0035.jpg
│ │ ├── 0036.jpg
│ │ ├── 0037.jpg
│ │ ├── 0038.jpg
│ │ ├── 0039.jpg
│ │ ├── 0040.jpg
│ │ ├── 0041.jpg
│ │ ├── 0042.jpg
│ │ ├── 0043.jpg
│ │ ├── 0044.jpg
│ │ ├── 0045.jpg
│ │ ├── 0046.jpg
│ │ ├── 0047.jpg
│ │ ├── 0048.jpg
│ │ ├── 0049.jpg
│ │ ├── 0050.jpg
│ │ ├── 0051.jpg
│ │ ├── 0052.jpg
│ │ ├── 0053.jpg
│ │ ├── 0054.jpg
│ │ └── 0055.jpg
│ └── tube.hdf5
└── visualize_video_tube.py
└── word_to_vec.py
/README.md:
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1 | # spatial-aware-object-embeddings
2 | This repository contains the basis for computing spatial-aware object embeddings for zero-shot action localization.
3 |
4 | The code is from the paper:
5 |
6 | _"Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions"_
7 |
8 | Pascal Mettes and Cees G. M. Snoek, International Conference on Computer Vision (ICCV), 2017
9 |
10 |
11 |
12 | The repository includes:
13 | * Object and actor extraction from pre-trained MS-COCO models.
14 | * Scoring of boxes in video frames for specified action names.
15 | * Linking of boxes into zero-shot action tubes.
16 | * word2vec and MS-COCO statistics required for zero-shot action localization and classification.
17 | * Config files, video lists, and train/test splits for common action datasets.
18 |
19 | ## Running the code
20 |
21 | The __first step__ is to adjust the paths for the action dataset of interest in the config file. This can be done either by personalizing the 'framedir' path, or by copying the frames per video to the pre-specified 'framedir' location.
22 |
23 | The __second step__ is to extract actor and object instances in each video frame. The code uses Faster R-CNN for such extraction. See their [Github page](https://github.com/rbgirshick/py-faster-rcnn/tree/master/data) for installation.
24 |
25 | The code is not limited to a specific model. Throughout the paper we employ a pre-trained Faster R-CNN model based on MS-COCO, which can be downloaded as follows:
26 | ```
27 | cd data/
28 | wget https://dl.dropboxusercontent.com/s/cotx0y81zvbbhnt/coco_vgg16_faster_rcnn_final.caffemodel?dl=0
29 | wget https://github.com/rbgirshick/py-faster-rcnn/blob/master/models/coco/VGG16/faster_rcnn_end2end/test.prototxt
30 | cd ..
31 | ```
32 |
33 | Given the Caffe models, the code for extraction can be run as follows (using UCF Sports as running example):
34 | ```
35 | python local_detect.py -c config/ucf-sports.config -p PROTOTXTFILE -m CAFFEMODELFILE
36 | ```
37 |
38 | The __third step__ is to score individual bounding boxes in video frames. There are multiple parameters here, the default parameters are as in the paper (see help option of the script for details):
39 | ```
40 | python local_score.py -c config/ucf-sports.config -t 1
41 | ```
42 |
43 | The __fourth step__ is to link local box scores into action tubes:
44 | ```
45 | python local_link.py -c config/ucf-sports.config -b PATH_FROM_LOCAL_SCORE -s 1 -t 5
46 | ```
47 |
48 | The local_detect.py, local_score.py, and local_link.py script all have a help option to see the parameters.
49 |
50 | ## Visualizing action tubes
51 |
52 | To facilitate a graphical display of action tubes and to gain quick insight into the correctness of a tube approach, we additionally provide a visualization script for action tubes.
53 |
54 | You can run the script directly to yield an example:
55 | ```
56 | python visualize_video_tube.py
57 | ```
58 | This should give the following result:
59 |
60 |
61 |
62 | For personalized visualizations, please read the script itself.
63 |
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/_init_paths.py:
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1 | # --------------------------------------------------------
2 | # Fast R-CNN
3 | # Copyright (c) 2015 Microsoft
4 | # Licensed under The MIT License [see LICENSE for details]
5 | # Written by Ross Girshick
6 | # --------------------------------------------------------
7 |
8 | """Set up paths for Fast R-CNN."""
9 |
10 | import os.path as osp
11 | import sys
12 |
13 | def add_path(path):
14 | if path not in sys.path:
15 | sys.path.insert(0, path)
16 |
17 | # Add caffe to PYTHONPATH
18 | caffe_path = osp.join('../../deep_localization/', 'py-faster-rcnn', 'caffe-fast-rcnn', 'python')
19 | #print caffe_path
20 | add_path(caffe_path)
21 |
22 | # Add lib to PYTHONPATH
23 | lib_path = osp.join('../../deep_localization/', 'py-faster-rcnn', 'lib')
24 | add_path(lib_path)
25 | #print lib_path
26 |
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/config/h2t.config:
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1 | [actions]
2 | # Dominant object / person detections.
3 | domdir = data/h2t/rpn/
4 |
5 | # Ground truth directory.
6 | gtdir = data/h2t/gt/
7 |
8 | # Directory for the action tubes.
9 | scoredir = data/h2t/boxscores/
10 |
11 | # Directory for the action tubes.
12 | tubedir = data/h2t/tubes/
13 |
14 | # Frames.
15 | framedir = data/h2t/videos/
16 |
17 | # Wordtovec file for the actions.
18 | wtvfile = data/h2t/wtv/sports-wtv.npy
19 |
20 | # Directory for the averaged supporting object scores.
21 | supportdir = data/h2t/global-objects/shuffle-avg/
22 |
23 | # Contextual video scores.
24 | videoscores = data/h2t/wtv/videoscores-wtv-shuffle.npy
25 |
26 | # Video file.
27 | vidfile = data/h2t/annotations/all_videos.txt
28 |
29 | # Train and test splits.
30 | trainsplit = data/h2t/annotations/trainlist.txt
31 | testsplit = data/h2t/annotations/testlist.txt
32 |
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/config/j-hmdb.config:
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1 | [actions]
2 | # Dominant object / person detections.
3 | domdir = data/j-hmdb/rpn/
4 |
5 | # Ground truth directory.
6 | gtdir = data/j-hmdb/gt/
7 |
8 | # Directory for the action tubes.
9 | scoredir = data/j-hmdb/boxscores/
10 |
11 | # Directory for the action tubes.
12 | tubedir = data/j-hmdb/tubes/
13 |
14 | # Frames.
15 | framedir = data/j-hmdb/videos/
16 |
17 | # Wordtovec file for the actions.
18 | wtvfile = data/j-hmdb/wtv/sports-wtv.npy
19 |
20 | # Directory for the averaged supporting object scores.
21 | supportdir = data/j-hmdb/global-objects/shuffle-avg/
22 |
23 | # Contextual video scores.
24 | videoscores = data/j-hmdb/wtv/videoscores-wtv-shuffle.npy
25 |
26 | # Video file.
27 | vidfile = data/j-hmdb/annotations/all_videos.txt
28 |
29 | # Train and test splits.
30 | trainsplit = data/j-hmdb/annotations/train-split1.txt
31 | testsplit = data/j-hmdb/annotations/test-split1.txt
32 |
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/config/ucf-101.config:
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1 | [actions]
2 | # Dominant object / person detections.
3 | domdir = data/ucf-101/rpn/
4 |
5 | # Ground truth directory.
6 | gtdir = data/ucf-101/gt/
7 |
8 | # Directory for the action tubes.
9 | scoredir = data/ucf-101/boxscores/
10 |
11 | # Directory for the action tubes.
12 | tubedir = data/ucf-101/tubes/
13 |
14 | # Frames.
15 | framedir = data/ucf-101/videos/
16 |
17 | # Wordtovec file for the actions.
18 | wtvfile = data/ucf-101/wtv/sports-wtv.npy
19 |
20 | # Directory for the averaged supporting object scores.
21 | supportdir = data/ucf-101/global-objects/shuffle-avg/
22 |
23 | # Contextual video scores.
24 | videoscores = data/ucf-101/wtv/videoscores-wtv-shuffle.npy
25 |
26 | # Video file.
27 | vidfile = data/ucf-101/annotations/all_videos.txt
28 |
29 | # Train and test splits.
30 | trainsplit = data/ucf-101/annotations/train_split01.txt
31 | testsplit = data/ucf-101/annotations/test_split01.txt
32 |
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/config/ucf-sports.config:
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1 | [actions]
2 | # Dominant object / person detections.
3 | domdir = data/ucf-sports/rpn/
4 |
5 | # Ground truth directory.
6 | gtdir = data/ucf-sports/gt/
7 |
8 | # Directory for the action tubes.
9 | scoredir = data/ucf-sports/boxscores/
10 |
11 | # Directory for the action tubes.
12 | tubedir = data/ucf-sports/tubes/
13 |
14 | # Frames.
15 | framedir = data/ucf-sports/videos/
16 |
17 | # Wordtovec file for the actions.
18 | wtvfile = data/ucf-sports/wtv/sports-wtv.npy
19 |
20 | # Directory for the averaged supporting object scores.
21 | supportdir = data/ucf-sports/global-objects/shuffle-avg/
22 |
23 | # Contextual video scores.
24 | videoscores = data/ucf-sports/wtv/videoscores-wtv-shuffle.npy
25 |
26 | # Video file.
27 | vidfile = data/ucf-sports/annotations/all_videos.txt
28 |
29 | # Train and test splits.
30 | trainsplit = data/ucf-sports/annotations/train_split.txt
31 | testsplit = data/ucf-sports/annotations/test_split.txt
32 |
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/data/embedding-example.png:
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https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/embedding-example.png
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/data/h2t/annotations/all_videos.txt:
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/data/h2t/annotations/testlist.txt:
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2 |
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/data/h2t/annotations/trainlist.txt:
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1 | 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873
2 |
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/data/h2t/wtv/h2t-wtv.npy:
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https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/h2t/wtv/h2t-wtv.npy
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/data/j-hmdb/annotations/all_videos.txt:
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1 | brush_hair/April_09_brush_hair_u_nm_np1_ba_goo_0
2 | brush_hair/April_09_brush_hair_u_nm_np1_ba_goo_1
3 | brush_hair/April_09_brush_hair_u_nm_np1_ba_goo_2
4 | brush_hair/Aussie_Brunette_Brushing_Long_Hair_brush_hair_u_nm_np1_ba_med_3
5 | brush_hair/Aussie_Brunette_Brushing_Long_Hair_brush_hair_u_nm_np1_fr_goo_1
6 | brush_hair/Aussie_Brunette_Brushing_Long_Hair_brush_hair_u_nm_np1_fr_goo_2
7 | brush_hair/Aussie_Brunette_Brushing_Long_Hair_brush_hair_u_nm_np1_fr_med_0
8 | brush_hair/Brushing_Hair_with_Beth_brush_hair_h_nm_np1_le_goo_0
9 | brush_hair/Brushing_Hair_with_Beth_brush_hair_u_nm_np1_fr_goo_1
10 | brush_hair/Brushing_Hair_with_Beth_brush_hair_u_nm_np1_fr_goo_2
11 | brush_hair/Brushing_hair__the_right_way_brush_hair_u_nm_np1_fr_goo_0
12 | brush_hair/Brushing_hair__the_right_way_brush_hair_u_nm_np1_fr_goo_1
13 | brush_hair/Brushing_hair__the_right_way_brush_hair_u_nm_np1_fr_goo_2
14 | brush_hair/Brushing_my_Long_Hair__February_2009_brush_hair_u_nm_np1_ba_goo_1
15 | brush_hair/Brushing_my_Long_Hair__February_2009_brush_hair_u_nm_np1_ba_goo_2
16 | brush_hair/Brushing_my_hair_-_December_2008_brush_hair_u_cm_np1_ba_goo_1
17 | brush_hair/Brushing_my_hair_-_December_2008_brush_hair_u_cm_np1_ba_goo_2
18 | brush_hair/Brushing_my_hair_-_December_2008_brush_hair_u_nm_np1_ba_goo_0
19 | brush_hair/Brushing_my_long_hair_brush_hair_u_nm_np1_ba_goo_2
20 | brush_hair/Brushing_my_long_hair_brush_hair_u_nm_np1_fr_goo_0
21 | brush_hair/Brushing_my_long_hair_brush_hair_u_nm_np1_fr_goo_1
22 | brush_hair/Ella_brushing_her_amazing_long_hair_brush_hair_u_cm_np1_ba_goo_0
23 | brush_hair/Ella_brushing_her_long_blonde_hair___again_brush_hair_u_cm_np1_ba_goo_0
24 | brush_hair/Ella_brushing_her_long_blonde_hair___again_brush_hair_u_cm_np1_ba_goo_2
25 | brush_hair/Ella_brushing_her_long_blonde_hair___again_brush_hair_u_cm_np1_le_goo_1
26 | brush_hair/My_Hair_Routine_brush_hair_h_nm_np1_le_goo_0
27 | brush_hair/Silky_Straight_Hair_Original_brush_hair_h_nm_np1_ba_goo_0
28 | brush_hair/Silky_Straight_Hair_Original_brush_hair_h_nm_np1_ba_goo_1
29 | brush_hair/Silky_Straight_Hair_Original_brush_hair_u_nm_np1_ba_goo_2
30 | brush_hair/Trannydude___Brushing_SyntheticHair___OhNOES!__those_fukin_knots!_brush_hair_u_nm_np1_fr_goo_0
31 | brush_hair/Trannydude___Brushing_SyntheticHair___OhNOES!__those_fukin_knots!_brush_hair_u_nm_np1_fr_goo_1
32 | brush_hair/Trannydude___Brushing_SyntheticHair___OhNOES!__those_fukin_knots!_brush_hair_u_nm_np1_fr_goo_2
33 | brush_hair/brush_my_hair_without_wearing_the_glasses_brush_hair_u_nm_np1_fr_goo_0
34 | brush_hair/brush_my_hair_without_wearing_the_glasses_brush_hair_u_nm_np1_fr_goo_2
35 | brush_hair/long_hair_Christa__brush_hair_u_nm_np1_le_goo_0
36 | brush_hair/me_brushing_my_hair_lol_i_was_being_silly_brush_hair_u_nm_np1_ba_goo_2
37 | brush_hair/me_brushing_my_hair_lol_i_was_being_silly_brush_hair_u_nm_np1_fr_goo_0
38 | brush_hair/me_brushing_my_hair_lol_i_was_being_silly_brush_hair_u_nm_np1_le_goo_1
39 | brush_hair/me_brushing_my_hair_lol_i_was_being_silly_brush_hair_u_nm_np1_ri_goo_3
40 | brush_hair/red_head_brush_hair_u_cm_np1_fr_goo_1
41 | brush_hair/red_head_brush_hair_u_cm_np1_le_goo_2
42 | catch/Ballfangen_catch_u_cm_np1_fr_goo_0
43 | catch/Ballfangen_catch_u_cm_np1_fr_goo_1
44 | catch/Ballfangen_catch_u_cm_np1_fr_goo_2
45 | catch/Faith_Rewarded_catch_f_cm_np1_fr_med_10
46 | catch/Frisbee_catch_f_cm_np1_ri_med_0
47 | catch/Frisbee_catch_f_cm_np1_ri_med_1
48 | catch/Goal_Keeping_Tips_catch_f_cm_np1_fr_med_0
49 | catch/Goal_Keeping_Tips_catch_f_cm_np1_fr_med_2
50 | catch/Goal_Keeping_Tips_catch_u_cm_np1_fr_med_1
51 | catch/Goal_Keeping_Tips_catch_u_cm_np1_fr_med_3
52 | catch/Goalkeeper_Training_Day_@_7_catch_f_cm_np1_ri_med_0
53 | catch/How_to_Play_Wide_Receiver_in_Football_-_How_to_Catch_a_High_Ball_in_Football_catch_f_nm_np1_ri_goo_0
54 | catch/How_to_Play_Wide_Receiver_in_Football_-_How_to_Catch_a_High_Ball_in_Football_catch_f_nm_np1_ri_med_1
55 | catch/LearnToShootFromTheMaster_catch_f_cm_np1_ba_med_6
56 | catch/LearnToShootFromTheMaster_catch_f_nm_np1_ba_med_0
57 | catch/LearnToShootFromTheMaster_catch_f_nm_np1_ba_med_1
58 | catch/LearnToShootFromTheMaster_catch_f_nm_np1_ba_med_4
59 | catch/Seldin_Lipovic_-_Willi_Weber__Torwarttraining__catch_f_cm_np1_ba_bad_7
60 | catch/Seldin_Lipovic_-_Willi_Weber__Torwarttraining__catch_f_cm_np1_ba_bad_8
61 | catch/Seldin_Lipovic_-_Willi_Weber__Torwarttraining__catch_f_cm_np1_fr_bad_3
62 | catch/Seldin_Lipovic_-_Willi_Weber__Torwarttraining__catch_f_cm_np1_fr_bad_5
63 | catch/Seldin_Lipovic_-_Willi_Weber__Torwarttraining__catch_f_cm_np1_fr_bad_6
64 | catch/Seldin_Lipovic_-_Willi_Weber__Torwarttraining__catch_f_cm_np1_le_bad_2
65 | catch/Seldin_Lipovic_-_Willi_Weber__Torwarttraining__catch_f_cm_np1_le_bad_4
66 | catch/Seldin_Lipovic_-_Willi_Weber__Torwarttraining__catch_f_cm_np1_le_med_1
67 | catch/Seldin_Lipovic_-_Willi_Weber__Torwarttraining__catch_u_cm_np1_fr_bad_0
68 | catch/Torwarttraining_-_Impressionen_vom_1__FFC_Frankfurt_catch_f_cm_np1_ri_med_0
69 | catch/Torwarttraining_-_Impressionen_vom_1__FFC_Frankfurt_catch_f_cm_np1_ri_med_1
70 | catch/Torwarttraining_-_Impressionen_vom_1__FFC_Frankfurt_catch_f_cm_np1_ri_med_2
71 | catch/Torwarttraining_2_(_sterreich)_catch_f_cm_np1_ba_goo_0
72 | catch/Torwarttraining_2_(_sterreich)_catch_f_cm_np1_ba_goo_1
73 | catch/Torwarttraining_2_(_sterreich)_catch_f_cm_np1_ba_goo_2
74 | catch/Torwarttraining_2_(_sterreich)_catch_f_cm_np1_le_med_3
75 | catch/Torwarttraining_2_(_sterreich)_catch_f_cm_np1_le_med_4
76 | catch/Torwarttraining_2_(_sterreich)_catch_f_cm_np1_le_med_5
77 | catch/Torwarttraining_3_(_sterreich)_catch_f_nm_np1_fr_med_3
78 | catch/Torwarttraining_3_(_sterreich)_catch_f_nm_np1_fr_med_4
79 | catch/Torwarttraining_3_(_sterreich)_catch_f_nm_np1_fr_med_5
80 | catch/Torwarttraining_3_(_sterreich)_catch_f_nm_np1_le_med_0
81 | catch/Torwarttraining_3_(_sterreich)_catch_f_nm_np1_le_med_1
82 | catch/Torwarttraining_3_(_sterreich)_catch_f_nm_np1_le_med_2
83 | catch/Torwarttraining_catch_f_cm_np1_le_bad_5
84 | catch/Torwarttraining_catch_u_cm_np1_fr_bad_4
85 | catch/Torwarttraining_catch_u_cm_np1_le_bad_6
86 | catch/Torwarttraining_catch_u_cm_np1_le_bad_7
87 | catch/Torwarttraining_catch_u_cm_np1_ri_med_0
88 | catch/Torwarttraining_catch_u_cm_np1_ri_med_1
89 | catch/Torwarttraining_catch_u_cm_np1_ri_med_2
90 | clap/103_years_old_japanese_woman__Nao_is_clapping_with_piano_music_by_beethoven_clap_u_cm_np1_fr_med_1
91 | clap/103_years_old_japanese_woman__Nao_is_clapping_with_piano_music_by_beethoven_clap_u_nm_np1_fr_med_0
92 | clap/@20_Rhythm_clap_u_nm_np1_fr_goo_0
93 | clap/@20_Rhythm_clap_u_nm_np1_fr_goo_1
94 | clap/@20_Rhythm_clap_u_nm_np1_le_goo_3
95 | clap/A_Round_of_Applause_clap_u_cm_np1_fr_med_0
96 | clap/A_Round_of_Applause_clap_u_cm_np1_fr_med_1
97 | clap/Alex_applauding_himself_clap_u_nm_np1_fr_med_0
98 | clap/Applauding_Abby_clap_u_nm_np1_fr_med_0
99 | clap/Applauding_Abby_clap_u_nm_np1_fr_med_1
100 | clap/Baby_Bob_kann_klatschen_!_clap_u_cm_np1_fr_med_0
101 | clap/Boom_Snap_Clap_clap_u_nm_np1_fr_med_0
102 | clap/Boom_Snap_Clap_clap_u_nm_np1_fr_med_1
103 | clap/Boom__Snap__Clap!_(Challenge)_clap_u_nm_np1_fr_med_1
104 | clap/Budam_-_Clap_Hands_clap_u_nm_np1_fr_med_0
105 | clap/Budam_-_Clap_Hands_clap_u_nm_np1_fr_med_1
106 | clap/Clap_Hands_clap_u_nm_np1_fr_med_2
107 | clap/Deutscher_Klatsch_-_The_Clap_-_Lexy_clap_f_cm_np1_fr_med_5
108 | clap/Flamenco_Dance_Steps_-_Flamenco_Dance_Soft_Claps_clap_u_cm_np1_fr_goo_1
109 | clap/Flamenco_Dance_Steps_-_Flamenco_Dance_Soft_Claps_clap_u_cm_np1_fr_goo_2
110 | clap/Kurt_Kr_mer_-_Klatschen_im_Flugzeug_clap_f_nm_np1_fr_med_0
111 | clap/Kurt_Kr_mer_-_Klatschen_im_Flugzeug_clap_u_cm_np1_fr_med_1
112 | clap/Kurt_Kr_mer_-_Klatschen_im_Flugzeug_clap_u_cm_np1_ri_bad_3
113 | clap/Kurt_Kr_mer_-_Klatschen_im_Flugzeug_clap_u_cm_np1_ri_bad_4
114 | clap/Martin_klatscht_in_die_h_nde_und_FURZT_clap_u_cm_np1_fr_med_0
115 | clap/RE-_Boom_Snap_Clap_clap_u_nm_np1_fr_med_0
116 | clap/RE-_Boom_Snap_Clap_clap_u_nm_np1_fr_med_1
117 | clap/Song_I_Can_Wave_My_Hands_-_Cullen_s_Abc_s_clap_u_cm_np1_fr_med_3
118 | clap/Song_I_Can_Wave_My_Hands_-_Cullen_s_Abc_s_clap_u_cm_np1_fr_med_4
119 | clap/The_Slow_Clap_clap_u_cm_np1_fr_med_4
120 | clap/The_Slow_Clap_clap_u_nm_np1_fr_bad_20
121 | clap/Theslowclap1_clap_f_cm_np1_fr_med_0
122 | clap/_Boom_Snap_Clap__challenge_clap_u_nm_np1_fr_med_0
123 | clap/_Boom_Snap_Clap__challenge_clap_u_nm_np1_fr_med_1
124 | clap/boom-snap-clap_clap_u_nm_np1_fr_med_0
125 | clap/boom-snap-clap_clap_u_nm_np1_fr_med_1
126 | clap/boom-snap-clap_clap_u_nm_np1_fr_med_2
127 | clap/boom-snap-clap_clap_u_nm_np1_fr_med_3
128 | clap/boom_snap_clap_(challenge)_HARDCORE_VERSION!_clap_u_nm_np1_fr_bad_0
129 | clap/boom_snap_clap_(challenge)_clap_u_nm_np1_fr_med_0
130 | clap/boom_snap_clap_(challenge)_clap_u_nm_np1_fr_med_1
131 | clap/boomsnapclap!_clap_u_nm_np1_fr_med_0
132 | clap/boomsnapclap!_clap_u_nm_np1_fr_med_1
133 | clap/boomsnapclap!_clap_u_nm_np1_fr_med_2
134 | climb_stairs/Baddest_Fight_Scenes_EVER!_-_Kill_Bill__Vol___1_-_vs__Crazy_88_s_climb_stairs_f_nm_np1_fr_med_3
135 | climb_stairs/Empire_State_Building_Run-Up_climb_stairs_f_cm_np1_ba_med_3
136 | climb_stairs/GuardiansOfO256kb_climb_stairs_f_cm_np1_ba_med_0
137 | climb_stairs/H_I_I_T__Swamis_stairs_with_Max_Wettstein_featuring_Donna_Wettstein_climb_stairs_f_cm_np1_ba_med_4
138 | climb_stairs/H_I_I_T__Swamis_stairs_with_Max_Wettstein_featuring_Donna_Wettstein_climb_stairs_f_cm_np1_ba_med_5
139 | climb_stairs/H_I_I_T__Swamis_stairs_with_Max_Wettstein_featuring_Donna_Wettstein_climb_stairs_f_cm_np1_ba_med_7
140 | climb_stairs/H_I_I_T__Swamis_stairs_with_Max_Wettstein_featuring_Donna_Wettstein_climb_stairs_f_cm_np1_ba_med_9
141 | climb_stairs/H_I_I_T__Swamis_stairs_with_Max_Wettstein_featuring_Donna_Wettstein_climb_stairs_f_cm_np1_fr_med_8
142 | climb_stairs/How_to_Exercise_Outdoors_-_Outdoor_Step_Running_Exercises_climb_stairs_f_cm_np1_fr_med_1
143 | climb_stairs/How_to_Exercise_Outdoors_-_Outdoor_Step_Running_Exercises_climb_stairs_f_nm_np1_fr_med_0
144 | climb_stairs/I_DO_NOT_Run_Up_Stairs_Like_A_GIRL_climb_stairs_l_cm_np1_ba_med_0
145 | climb_stairs/I_DO_NOT_Run_Up_Stairs_Like_A_GIRL_climb_stairs_l_cm_np1_ba_med_1
146 | climb_stairs/I_DO_NOT_Run_Up_Stairs_Like_A_GIRL_climb_stairs_l_cm_np1_ba_med_2
147 | climb_stairs/I_DO_NOT_Run_Up_Stairs_Like_A_GIRL_climb_stairs_l_cm_np1_ba_med_3
148 | climb_stairs/I_DO_NOT_Run_Up_Stairs_Like_A_GIRL_climb_stairs_l_cm_np1_ba_med_4
149 | climb_stairs/I_DO_NOT_Run_Up_Stairs_Like_A_GIRL_climb_stairs_l_cm_np1_ba_med_5
150 | climb_stairs/Intense_Cardio_Session-_Stairs_climb_stairs_f_cm_np1_ba_med_0
151 | climb_stairs/Intense_Cardio_Session-_Stairs_climb_stairs_f_cm_np1_ba_med_2
152 | climb_stairs/Micah_Lacerte_Running_Stairs___Great_cardio_for_fat_loss_climb_stairs_f_cm_np1_ba_med_3
153 | climb_stairs/Micah_Lacerte_Running_Stairs___Great_cardio_for_fat_loss_climb_stairs_f_cm_np1_ba_med_4
154 | climb_stairs/Micah_Lacerte_Running_Stairs___Great_cardio_for_fat_loss_climb_stairs_f_cm_np1_fr_med_1
155 | climb_stairs/Micah_Lacerte_Running_Stairs___Great_cardio_for_fat_loss_climb_stairs_f_cm_np1_fr_med_2
156 | climb_stairs/Outdoor_Agility_Training_-_Outdoor_Stair_Run_Drill_for_Agility_climb_stairs_f_cm_np1_ba_med_0
157 | climb_stairs/Outdoor_Agility_Training_-_Outdoor_Stair_Run_Drill_for_Agility_climb_stairs_f_cm_np1_ba_med_1
158 | climb_stairs/Outdoor_Agility_Training_-_Outdoor_Stair_Run_Drill_for_Agility_climb_stairs_f_cm_np1_ba_med_2
159 | climb_stairs/Sports_Training_-_Speed_Workouts_-_Stair_Running_climb_stairs_f_nm_np1_ba_med_0
160 | climb_stairs/Sports_Training_-_Speed_Workouts_-_Stair_Running_climb_stairs_f_nm_np1_ba_med_1
161 | climb_stairs/Sports_Training_-_Speed_Workouts_-_Stair_Running_climb_stairs_f_nm_np1_ba_med_2
162 | climb_stairs/Stadium_Plyometric_Workout_climb_stairs_f_cm_np1_ba_bad_5
163 | climb_stairs/Stadium_Plyometric_Workout_climb_stairs_l_cm_np1_ba_med_3
164 | climb_stairs/Stadium_Plyometric_Workout_climb_stairs_l_cm_np1_ba_med_4
165 | climb_stairs/Stadium_Plyometric_Workout_climb_stairs_l_cm_np1_ri_med_0
166 | climb_stairs/Stadium_Plyometric_Workout_climb_stairs_l_cm_np1_ri_med_2
167 | climb_stairs/Stairway_to_fitness_climb_stairs_f_nm_np1_fr_med_0
168 | climb_stairs/Stairway_to_fitness_climb_stairs_f_nm_np1_fr_med_1
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672 | sit/A_Beautiful_Mind_3_sit_f_cm_np1_le_med_4
673 | sit/AllThePresidentMen_sit_f_nm_np1_fr_goo_3
674 | sit/AllThePresidentMen_sit_f_nm_np1_ri_med_7
675 | sit/AmericanGangster_sit_f_cm_np1_fr_med_70
676 | sit/American_History_X_sit_f_cm_np1_le_med_27
677 | sit/American_History_X_sit_u_cm_np1_fr_goo_39
678 | sit/American_History_X_sit_u_cm_np1_fr_med_6
679 | sit/American_History_X_sit_u_cm_np1_fr_med_8
680 | sit/BIG_FISH_sit_f_nm_np1_fr_med_17
681 | sit/BRIDGETOTERABITHIA_sit_f_nm_np1_fr_med_0
682 | sit/CharlieAndTheChocolateFactory_sit_f_nm_np1_fr_med_3
683 | sit/CharlieAndTheChocolateFactory_sit_f_nm_np1_le_med_7
684 | sit/EasternPromises_sit_f_cm_np1_ri_med_3
685 | sit/EasternPromises_sit_f_nm_np1_ri_med_4
686 | sit/HP_PRISONER_OF_AZKABAN_sit_f_cm_np1_le_med_1
687 | sit/IamLegendII_sit_f_nm_np1_le_med_4
688 | sit/IamLegend_sit_f_cm_np1_fr_med_24
689 | sit/Juno_sit_u_cm_np1_fr_goo_7
690 | sit/NoCountryForOldMen_sit_f_nm_np1_fr_goo_4
691 | sit/NoCountryForOldMen_sit_f_nm_np1_ri_med_6
692 | sit/Prelinger_ActYourA1949_sit_u_cm_np1_ri_med_2
693 | sit/Prelinger_HabitPat1954_sit_u_cm_np1_le_med_8
694 | sit/RETURN_OF_THE_KING_sit_u_cm_np1_le_med_7
695 | sit/Shadow_of_a_Doubt_sit_u_cm_np1_fr_med_11
696 | sit/TheBoondockSaints_sit_u_cm_np1_le_med_15
697 | sit/TheLastManOnearth_sit_f_nm_np1_ri_med_51
698 | sit/TheLittleShopofHorrors_sit_f_nm_np1_ri_med_7
699 | sit/ThePerfectScore_sit_u_cm_np1_fr_med_1
700 | sit/ThePerfectScore_sit_u_cm_np1_fr_med_2
701 | sit/The_Fugitive_2_sit_f_cm_np1_fr_med_8
702 | sit/ThreeStories_sit_u_cm_np1_fr_med_1
703 | sit/TrumanShow_sit_f_nm_np1_le_med_37
704 | sit/Veoh_Alpha_Dog_2_sit_f_nm_np1_fr_med_0
705 | sit/likebeckam_sit_f_nm_np1_le_bad_29
706 | sit/nameunknown256kb_sit_u_nm_np1_fr_med_1
707 | sit/prelinger_ControlY1950_sit_f_cm_np1_fr_med_1
708 | sit/prelinger_LetsPlay1949_sit_u_cm_np1_le_med_9
709 | sit/prideandprejudice1_sit_f_nm_np1_ri_med_10
710 | stand/20060723sfjffangelina_stand_f_nm_np1_le_med_0
711 | stand/20060723sfjffjewgotmail_stand_u_cm_np1_ri_med_0
712 | stand/A_Beautiful_Mind_4_stand_f_cm_np1_fr_med_13
713 | stand/A_Beautiful_Mind_6_stand_u_cm_np1_fr_med_3
714 | stand/AmericanGangster_stand_u_nm_np1_fr_med_55
715 | stand/BATMAN_BEGINS_stand_f_cm_np1_fr_med_8
716 | stand/BIG_FISH_stand_u_nm_np1_fr_med_27
717 | stand/BRIDGETOTERABITHIA_stand_f_nm_np1_fr_med_1
718 | stand/Crash_stand_f_cm_np1_le_med_15
719 | stand/Crash_stand_f_cm_np1_ri_bad_12
720 | stand/Crash_stand_u_nm_np1_le_med_6
721 | stand/EVOLUTION_stand_f_nm_np1_le_med_15
722 | stand/Fellowship_4_stand_f_cm_np1_fr_med_3
723 | stand/HP_PRISONER_OF_AZKABAN_stand_f_cm_np1_fr_med_4
724 | stand/HP_PRISONER_OF_AZKABAN_stand_f_cm_np1_le_bad_38
725 | stand/IamLegend_stand_f_cm_np1_fr_bad_27
726 | stand/Man_Who_Cheated_Himself_512kb_stand_u_cm_np1_fr_med_11
727 | stand/Panic_in_the_Streets_stand_u_cm_np1_ri_med_2
728 | stand/Prelinger_LetYours1940_stand_f_nm_np1_fr_med_4
729 | stand/RATRACE_stand_f_cm_np1_le_med_35
730 | stand/RATRACE_stand_f_cm_np1_ri_med_28
731 | stand/RETURN_OF_THE_KING_stand_u_cm_np1_fr_med_12
732 | stand/RushHour2_stand_u_cm_np1_fr_med_11
733 | stand/SchoolRulesHowTheyHelpUs_stand_u_nm_np1_fr_med_1
734 | stand/SoundAndTheStory_stand_f_cm_np1_ri_med_0
735 | stand/SweeneyTodd_stand_f_cm_np1_le_med_10
736 | stand/SweeneyTodd_stand_f_cm_np1_ri_med_33
737 | stand/THE_PROTECTOR_stand_f_cm_np1_fr_med_23
738 | stand/TheLastManOnearth_stand_f_nm_np1_fr_med_24
739 | stand/happy_go_lovely_stand_f_cm_np1_fr_med_19
740 | stand/happy_go_lovely_stand_u_cm_np1_fr_bad_14
741 | stand/happy_go_lovely_stand_u_nm_np1_fr_med_9
742 | stand/jonhs_netfreemovies_holygrail_stand_u_nm_np1_fr_bad_15
743 | stand/likebeckam_stand_f_nm_np1_fr_med_7
744 | stand/prelinger_ControlY1950_stand_f_cm_np1_fr_med_2
745 | stand/prelinger_ControlY1950_stand_u_cm_np1_fr_med_0
746 | swing_baseball/8YearOldHitsaHomeRunBaseball_swing_baseball_f_cm_np1_fr_bad_0
747 | swing_baseball/BaseballSwingAnalysis_swing_baseball_f_nm_np1_fr_med_11
748 | swing_baseball/BaseballSwingAnalysis_swing_baseball_f_nm_np1_fr_med_12
749 | swing_baseball/BaseballSwingAnalysis_swing_baseball_f_nm_np1_fr_med_8
750 | swing_baseball/BaseballSwingAnalysis_swing_baseball_f_nm_np1_fr_med_9
751 | swing_baseball/BaseballSwingAnalysis_swing_baseball_u_nm_np1_ba_med_0
752 | swing_baseball/BaseballSwingAnalysis_swing_baseball_u_nm_np1_ba_med_1
753 | swing_baseball/BaseballSwingAnalysis_swing_baseball_u_nm_np1_ba_med_2
754 | swing_baseball/BaseballSwingAnalysis_swing_baseball_u_nm_np1_ba_med_3
755 | swing_baseball/BaseballSwingAnalysis_swing_baseball_u_nm_np1_ba_med_4
756 | swing_baseball/BaseballSwingAnalysis_swing_baseball_u_nm_np1_fr_med_10
757 | swing_baseball/BaseballSwingAnalysis_swing_baseball_u_nm_np1_fr_med_13
758 | swing_baseball/Faith_Rewarded_swing_baseball_f_cm_np1_ba_bad_11
759 | swing_baseball/Faith_Rewarded_swing_baseball_f_nm_np0_fr_bad_67
760 | swing_baseball/Faith_Rewarded_swing_baseball_f_nm_np1_ba_bad_22
761 | swing_baseball/Faith_Rewarded_swing_baseball_f_nm_np1_ba_med_23
762 | swing_baseball/Faith_Rewarded_swing_baseball_f_nm_np1_fr_bad_26
763 | swing_baseball/Faith_Rewarded_swing_baseball_f_nm_np1_fr_bad_72
764 | swing_baseball/Faith_Rewarded_swing_baseball_f_nm_np1_fr_med_75
765 | swing_baseball/HittingaSingle_swing_baseball_f_cm_np1_fr_med_0
766 | swing_baseball/Hittingadouble(BXbaseball)_swing_baseball_f_nm_np1_ba_bad_0
767 | swing_baseball/HowtoswingaBaseballbat_swing_baseball_f_nm_np1_le_bad_0
768 | swing_baseball/Tannerafterwecorrected_swing_baseball_f_cm_np1_fr_bad_0
769 | swing_baseball/Tannerafterwecorrected_swing_baseball_f_cm_np1_fr_bad_1
770 | swing_baseball/Tannerafterwecorrected_swing_baseball_f_cm_np1_fr_bad_2
771 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_0
772 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_1
773 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_10
774 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_11
775 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_3
776 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_4
777 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_5
778 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_6
779 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_7
780 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_8
781 | swing_baseball/hittingofftee2_swing_baseball_f_nm_np1_fr_med_9
782 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_12
783 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_13
784 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_14
785 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_15
786 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_16
787 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_17
788 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_18
789 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_19
790 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_2
791 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_4
792 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_5
793 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_6
794 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_7
795 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_8
796 | swing_baseball/practicingmybaseballswing2009_swing_baseball_f_cm_np1_fr_med_9
797 | swing_baseball/practicingmybaseballswing2009_swing_baseball_u_cm_np1_fr_med_0
798 | swing_baseball/practicingmybaseballswing2009_swing_baseball_u_cm_np1_fr_med_1
799 | swing_baseball/practicingmybaseballswing2009_swing_baseball_u_cm_np1_fr_med_10
800 | throw/AboutABoy_throw_f_nm_np1_ba_med_2
801 | throw/AboutABoy_throw_u_nm_np1_ba_med_3
802 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_0
803 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_1
804 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_10
805 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_11
806 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_12
807 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_13
808 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_14
809 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_15
810 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_16
811 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_17
812 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_18
813 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_19
814 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_2
815 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_3
816 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_4
817 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_5
818 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_6
819 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_7
820 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_8
821 | throw/Compilationknifethrowing_throw_u_nm_np1_ba_med_9
822 | throw/Faith_Rewarded_throw_f_nm_np1_le_bad_51
823 | throw/Faith_Rewarded_throw_f_nm_np1_ri_bad_9
824 | throw/Faith_Rewarded_throw_u_cm_np1_fr_med_33
825 | throw/HowtoParticipateinKnife_throw_f_cm_np1_le_med_1
826 | throw/HowtoParticipateinKnife_throw_u_cm_np1_ba_med_0
827 | throw/KnifeThrowingJackDaggerDemoReel_throw_f_nm_np1_fr_med_1
828 | throw/KnifeThrowingJackDaggerDemoReel_throw_u_cm_np1_ba_med_6
829 | throw/KnifeThrowingJackDaggerDemoReel_throw_u_cm_np1_ri_med_7
830 | throw/KnifeThrowing_throw_f_nm_np1_le_med_0
831 | throw/KnifeThrowing_throw_f_nm_np1_le_med_1
832 | throw/KnifeThrowing_throw_f_nm_np1_le_med_2
833 | throw/KnifeThrowing_throw_f_nm_np1_le_med_3
834 | throw/KnifeThrowing_throw_f_nm_np1_le_med_4
835 | throw/Knifethrowing4knivestogethernospin_throw_f_nm_np1_ri_med_1
836 | throw/Knifethrowing4knivestogethernospin_throw_u_nm_np1_ba_med_0
837 | throw/Prelinger_ActYourA1949_throw_u_nm_np1_ri_med_13
838 | throw/ScottKazmir_throw_f_cm_np1_fr_med_0
839 | throw/ScottKazmir_throw_f_cm_np1_fr_med_2
840 | throw/Shadow_of_a_Doubt_throw_u_cm_np1_ba_med_0
841 | throw/Veoh_Alpha_Dog_1_throw_f_cm_np1_le_med_5
842 | throw/Veoh_Alpha_Dog_1_throw_u_nm_np1_ri_med_25
843 | throw/amazingballthrowingtricks_throw_f_cm_np1_ri_med_5
844 | throw/amazingballthrowingtricks_throw_f_nm_np1_ri_med_0
845 | throw/prelinger_they_grow_up_so_fast_1_throw_f_nm_np1_fr_med_8
846 | walk/20060723sfjffkillerskiss_walk_f_cm_np1_fr_med_1
847 | walk/50_FIRST_DATES_walk_f_cm_np1_le_med_33
848 | walk/AgriculturalAviation_walk_f_nm_np1_le_med_1
849 | walk/AmericanGangster_walk_f_nm_np1_ba_med_72
850 | walk/AmericanGangster_walk_f_nm_np1_fr_med_38
851 | walk/BIG_FISH_walk_f_nm_np1_fr_med_2
852 | walk/EVOLUTION_walk_f_cm_np1_fr_med_23
853 | walk/Eurotrip_walk_f_nm_np1_fr_med_8
854 | walk/HP_PRISONER_OF_AZKABAN_walk_f_cm_np1_ri_med_2
855 | walk/Juno_walk_f_cm_np1_fr_med_8
856 | walk/LONGESTYARD_walk_f_nm_np1_fr_med_6
857 | walk/MeettheFockers_walk_f_nm_np1_ba_med_9
858 | walk/NOVA_ELEGANTUNIVERSE2_walk_f_nm_np1_ba_med_0
859 | walk/NOVA_ELEGANTUNIVERSE2_walk_u_nm_np1_ri_med_3
860 | walk/Oceans12_walk_u_cm_np1_fr_med_10
861 | walk/OldSchool_walk_f_nm_np1_ri_med_0
862 | walk/Panic_in_the_Streets_walk_u_cm_np1_ba_med_5
863 | walk/Panic_in_the_Streets_walk_u_nm_np1_fr_med_4
864 | walk/Prelinger_LetYours1940_walk_f_nm_np1_ba_med_5
865 | walk/RATRACE_walk_f_cm_np1_ba_med_17
866 | walk/RETURN_OF_THE_KING_walk_f_cm_np1_ba_med_3
867 | walk/SafeInPort_walk_f_nm_np1_ba_med_2
868 | walk/SchoolRulesHowTheyHelpUs_walk_f_cm_np1_fr_med_2
869 | walk/THE_PROTECTOR_walk_f_cm_np1_ba_med_27
870 | walk/TheLastManOnearth_walk_f_cm_np1_ba_med_25
871 | walk/TheLastManOnearth_walk_f_cm_np1_ba_med_27
872 | walk/TheLastManOnearth_walk_f_cm_np1_fr_med_36
873 | walk/TheLastManOnearth_walk_f_cm_np1_ri_med_2
874 | walk/TheLastManOnearth_walk_f_nm_np1_ba_med_26
875 | walk/TheLastManOnearth_walk_f_nm_np1_fr_med_21
876 | walk/The_Fugitive_2_walk_u_cm_np1_fr_med_13
877 | walk/The_Fugitive_3_walk_f_cm_np1_ba_bad_0
878 | walk/The_House_on_Haunted_Hill_walk_f_cm_np1_fr_med_18
879 | walk/The_House_on_Haunted_Hill_walk_u_cm_np1_fr_med_14
880 | walk/TrumanShow_walk_f_cm_np1_ri_med_36
881 | walk/Veoh_Alpha_Dog_1_walk_f_nm_np1_ri_med_24
882 | walk/Veoh_Alpha_Dog_1_walk_u_cm_np1_fr_med_13
883 | walk/Veoh_Alpha_Dog_2_walk_f_cm_np2_ri_med_10
884 | walk/Veoh_Alpha_Dog_2_walk_u_nm_np1_le_med_29
885 | walk/likebeckam_walk_f_cm_np1_ri_med_31
886 | walk/prelinger_LetsPlay1949_walk_f_nm_np1_le_med_10
887 | wave/20060723sfjffbartsinger_wave_f_cm_np1_ba_med_0
888 | wave/21_wave_u_nm_np1_fr_goo_5
889 | wave/50_FIRST_DATES_wave_u_cm_np1_fr_goo_30
890 | wave/Abschieds-winken_!!!!!!_Gruss_Guido_wave_u_cm_np1_fr_bad_1
891 | wave/American_History_X_wave_u_nm_np1_fr_med_16
892 | wave/BATMAN_BEGINS_wave_u_cm_np1_fr_med_19
893 | wave/BIG_FISH_wave_u_nm_np1_fr_bad_4
894 | wave/BIG_FISH_wave_u_nm_np1_fr_goo_9
895 | wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_fr_med_0
896 | wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_fr_med_1
897 | wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_fr_med_2
898 | wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_fr_med_3
899 | wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_fr_med_4
900 | wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_fr_med_8
901 | wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_fr_med_9
902 | wave/Bush_Wave_vs__Obama_Wave_and_Which_Commander-in-chief_Salutes_the_Best__You_Decide_wave_u_cm_np1_le_med_6
903 | wave/CharlieAndTheChocolateFactory_wave_u_nm_np1_fr_bad_1
904 | wave/Chris_Benoit_waving_goodbye_wave_u_nm_np1_fr_med_0
905 | wave/Chris_Benoit_waving_goodbye_wave_u_nm_np1_fr_med_1
906 | wave/DONNIE_DARKO_wave_u_nm_np1_fr_goo_9
907 | wave/EVOLUTION_wave_u_cm_np1_fr_med_4
908 | wave/EVOLUTION_wave_u_nm_np1_fr_med_18
909 | wave/Hands_Up__Clap_Hands_And_Wave!_wave_f_cm_np1_ri_med_0
910 | wave/Hitch_Part_2_wave_h_nm_np1_fr_goo_12
911 | wave/Maddin_winkt_wave_h_cm_np1_fr_med_1
912 | wave/Maya_beim_Winken_wave_f_cm_np1_fr_med_0
913 | wave/Maya_beim_Winken_wave_f_cm_np1_fr_med_1
914 | wave/Maya_beim_Winken_wave_f_cm_np1_fr_med_2
915 | wave/OldSchool_wave_f_cm_np1_fr_med_13
916 | wave/Pirates_5_wave_h_nm_np1_fr_med_8
917 | wave/RATRACE_wave_f_nm_np1_ri_med_10
918 | wave/Song_I_Can_Wave_My_Hands_-_Cullen_s_Abc_s_wave_u_cm_np1_fr_med_0
919 | wave/Song_I_Can_Wave_My_Hands_-_Cullen_s_Abc_s_wave_u_cm_np1_fr_med_1
920 | wave/Song_I_Can_Wave_My_Hands_-_Cullen_s_Abc_s_wave_u_cm_np1_fr_med_2
921 | wave/TrumanShow_wave_u_nm_np1_fr_med_0
922 | wave/TrumanShow_wave_u_nm_np1_fr_med_25
923 | wave/WeddingCrashers_wave_u_nm_np1_fr_goo_0
924 | wave/Wie_man_winkt!!_wave_u_cm_np1_fr_med_0
925 | wave/eddieizzardcircle_wave_u_nm_np1_ri_med_8
926 | wave/prideandprejudice1_wave_f_nm_np1_ri_med_14
927 | wave/veoh_harold_and_kumar_wave_u_nm_np1_fr_goo_5
928 | wave/wave_and_say_hi_wave_u_nm_np1_fr_med_0
929 |
--------------------------------------------------------------------------------
/data/j-hmdb/annotations/test-split1.txt:
--------------------------------------------------------------------------------
1 | 1 2 3 8 9 10 16 17 18 22 33 34 46 47 52 55 56 57 58 83 84 85 86 87 88 89 90 91 98 99 100 101 102 104 105 108 109 119 120 136 144 145 146 147 148 149 167 168 171 172 173 174 175 178 179 181 193 194 198 203 212 213 214 235 236 237 238 239 240 244 245 251 252 253 254 259 265 266 267 268 269 277 286 287 288 290 294 298 299 300 301 303 305 306 309 310 311 320 331 332 333 334 335 336 337 342 343 350 351 352 356 365 366 367 397 398 399 403 404 405 406 407 408 425 426 427 428 429 430 431 441 442 457 458 464 465 466 467 476 477 478 479 490 495 497 500 502 503 504 515 516 517 518 540 541 542 554 555 556 557 558 559 560 561 567 568 569 570 571 572 594 595 602 603 604 605 606 607 615 629 630 631 632 633 634 635 636 637 638 639 649 650 651 660 661 673 674 675 682 683 686 690 691 692 693 703 705 713 715 722 727 732 734 735 736 743 744 745 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 830 831 832 833 834 835 836 841 842 843 844 847 849 850 852 855 856 862 863 864 869 883 884 887 889 891 893 894 904 905 912 913 914 923 927
2 |
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/data/j-hmdb/annotations/train-split1.txt:
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1 | 4 5 6 7 19 20 21 26 36 37 38 39 11 12 13 23 24 25 30 31 32 27 28 29 40 41 35 14 15 53 54 71 72 73 74 75 76 59 60 61 62 63 64 65 66 67 77 78 79 80 81 82 45 48 49 50 51 42 43 44 68 69 70 115 116 129 130 117 118 103 110 111 112 113 97 95 96 124 125 126 127 106 92 93 94 121 114 128 122 123 131 132 133 107 159 160 161 169 170 156 157 158 162 163 164 165 166 152 153 154 155 142 143 150 151 134 137 138 139 140 141 135 186 187 188 189 190 191 209 196 205 206 197 215 202 177 201 176 185 200 211 204 199 207 208 210 192 182 183 184 195 180 228 229 230 231 232 233 250 241 220 221 222 223 224 225 226 227 216 217 218 219 249 242 243 246 247 248 234 263 264 255 256 260 276 270 257 258 271 272 273 274 275 278 279 280 281 282 283 284 285 289 261 262 295 291 314 308 292 293 319 307 313 317 318 315 316 312 324 325 326 296 297 302 304 329 330 327 328 321 322 323 338 359 360 339 340 341 383 384 385 362 363 364 349 380 381 382 346 347 348 344 345 374 375 376 357 358 371 372 373 353 354 355 361 377 378 379 368 369 370 413 414 415 412 416 417 418 386 387 388 419 420 421 409 410 411 422 423 424 395 396 392 393 394 389 390 391 435 436 437 438 439 440 432 433 434 400 401 402 452 472 470 471 443 473 474 475 450 451 447 448 449 468 469 459 460 461 462 463 480 481 482 444 445 446 453 454 455 456 483 501 522 498 505 499 506 507 508 509 510 514 496 519 520 521 484 511 512 513 485 486 487 488 489 494 491 492 493 538 539 536 537 523 524 525 526 527 528 529 530 531 532 533 534 535 562 543 544 545 546 547 548 549 550 551 552 553 563 564 596 597 598 599 600 601 592 593 573 574 565 566 608 609 610 611 612 613 614 591 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 628 646 647 648 617 662 663 664 665 666 667 668 669 670 655 656 657 658 659 652 653 654 640 641 642 643 644 645 618 619 620 621 623 624 625 626 627 616 622 704 706 707 694 680 687 689 698 688 701 709 696 681 676 677 678 679 697 684 685 708 702 671 672 699 700 695 712 725 733 721 737 738 711 718 719 720 714 717 723 724 728 726 710 739 740 741 716 729 730 731 742 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 765 758 759 760 761 762 763 764 746 766 747 748 749 750 751 752 753 754 755 756 757 800 801 825 826 845 838 839 837 827 828 829 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 840 822 823 824 854 865 876 858 859 861 860 870 871 872 873 874 875 881 882 878 879 885 868 867 853 857 880 848 846 877 886 866 851 915 925 906 895 896 897 898 899 900 901 902 926 918 919 920 892 909 916 888 917 890 928 910 903 907 908 924 911 921 922
2 |
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/data/j-hmdb/wtv/jhmdb-wtv.npy:
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https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/j-hmdb/wtv/jhmdb-wtv.npy
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/data/mscoco/mscoco-wtv.npy:
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https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/mscoco/mscoco-wtv.npy
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/data/mscoco/mscoco_classes.txt:
--------------------------------------------------------------------------------
1 | person
2 | bicycle
3 | car
4 | motorcycle
5 | airplane
6 | bus
7 | train
8 | truck
9 | boat
10 | traffic light
11 | fire hydrant
12 | stop sign
13 | parking meter
14 | bench
15 | bird
16 | cat
17 | dog
18 | horse
19 | sheep
20 | cow
21 | elephant
22 | bear
23 | zebra
24 | giraffe
25 | backpack
26 | umbrella
27 | handbag
28 | tie
29 | suitcase
30 | frisbee
31 | skis
32 | snowboard
33 | sports ball
34 | kite
35 | baseball bat
36 | baseball glove
37 | skateboard
38 | surfboard
39 | tennis racket
40 | bottle
41 | wine glass
42 | cup
43 | fork
44 | knife
45 | spoon
46 | bowl
47 | banana
48 | apple
49 | sandwich
50 | orange
51 | broccoli
52 | carrot
53 | hot dog
54 | pizza
55 | donut
56 | cake
57 | chair
58 | couch
59 | potted plant
60 | bed
61 | dining table
62 | toilet
63 | tv
64 | laptop
65 | mouse
66 | remote
67 | keyboard
68 | cell phone
69 | microwave
70 | oven
71 | toaster
72 | sink
73 | refrigerator
74 | book
75 | clock
76 | vase
77 | scissors
78 | teddy bear
79 | hair drier
80 | toothbrush
81 |
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/data/mscoco/mscoco_priors_new.npy:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/mscoco/mscoco_priors_new.npy
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/data/ucf-101/annotations/test_split01.txt:
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1 | 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207
2 |
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/data/ucf-101/annotations/train_split01.txt:
--------------------------------------------------------------------------------
1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293
2 |
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/data/ucf-101/wtv/ucf-101-wtv.npy:
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https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/ucf-101/wtv/ucf-101-wtv.npy
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/data/ucf-sports/annotations/all_videos.txt:
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1 | diving/Diving-Side/001
2 | diving/Diving-Side/002
3 | diving/Diving-Side/003
4 | diving/Diving-Side/004
5 | diving/Diving-Side/005
6 | diving/Diving-Side/006
7 | diving/Diving-Side/007
8 | diving/Diving-Side/008
9 | diving/Diving-Side/009
10 | diving/Diving-Side/010
11 | diving/Diving-Side/011
12 | diving/Diving-Side/012
13 | diving/Diving-Side/013
14 | diving/Diving-Side/014
15 | swinging-golf/Golf-Swing-Back/001
16 | swinging-golf/Golf-Swing-Back/002
17 | swinging-golf/Golf-Swing-Back/003
18 | swinging-golf/Golf-Swing-Back/004
19 | swinging-golf/Golf-Swing-Back/005
20 | swinging-golf/Golf-Swing-Front/001
21 | swinging-golf/Golf-Swing-Front/002
22 | swinging-golf/Golf-Swing-Front/003
23 | swinging-golf/Golf-Swing-Front/004
24 | swinging-golf/Golf-Swing-Front/005
25 | swinging-golf/Golf-Swing-Front/006
26 | swinging-golf/Golf-Swing-Front/007
27 | swinging-golf/Golf-Swing-Front/008
28 | swinging-golf/Golf-Swing-Side/001
29 | swinging-golf/Golf-Swing-Side/002
30 | swinging-golf/Golf-Swing-Side/003
31 | swinging-golf/Golf-Swing-Side/004
32 | swinging-golf/Golf-Swing-Side/005
33 | kicking/Kicking-Front/001
34 | kicking/Kicking-Front/002
35 | kicking/Kicking-Front/003
36 | kicking/Kicking-Front/004
37 | kicking/Kicking-Front/005
38 | kicking/Kicking-Front/006
39 | kicking/Kicking-Front/007
40 | kicking/Kicking-Front/008
41 | kicking/Kicking-Front/009
42 | kicking/Kicking-Front/010
43 | kicking/Kicking-Side/001
44 | kicking/Kicking-Side/002
45 | kicking/Kicking-Side/003
46 | kicking/Kicking-Side/004
47 | kicking/Kicking-Side/005
48 | kicking/Kicking-Side/006
49 | kicking/Kicking-Side/007
50 | kicking/Kicking-Side/008
51 | kicking/Kicking-Side/009
52 | kicking/Kicking-Side/010
53 | lifting/Lifting/001
54 | lifting/Lifting/002
55 | lifting/Lifting/003
56 | lifting/Lifting/004
57 | lifting/Lifting/005
58 | lifting/Lifting/006
59 | riding-horse/Riding-Horse/001
60 | riding-horse/Riding-Horse/002
61 | riding-horse/Riding-Horse/003
62 | riding-horse/Riding-Horse/004
63 | riding-horse/Riding-Horse/005
64 | riding-horse/Riding-Horse/006
65 | riding-horse/Riding-Horse/007
66 | riding-horse/Riding-Horse/008
67 | riding-horse/Riding-Horse/009
68 | riding-horse/Riding-Horse/010
69 | riding-horse/Riding-Horse/011
70 | riding-horse/Riding-Horse/012
71 | running/Run-Side/001
72 | running/Run-Side/002
73 | running/Run-Side/003
74 | running/Run-Side/004
75 | running/Run-Side/005
76 | running/Run-Side/006
77 | running/Run-Side/007
78 | running/Run-Side/008
79 | running/Run-Side/009
80 | running/Run-Side/010
81 | running/Run-Side/011
82 | running/Run-Side/012
83 | running/Run-Side/013
84 | skateboarding/SkateBoarding-Front/001
85 | skateboarding/SkateBoarding-Front/002
86 | skateboarding/SkateBoarding-Front/003
87 | skateboarding/SkateBoarding-Front/004
88 | skateboarding/SkateBoarding-Front/005
89 | skateboarding/SkateBoarding-Front/006
90 | skateboarding/SkateBoarding-Front/007
91 | skateboarding/SkateBoarding-Front/008
92 | skateboarding/SkateBoarding-Front/009
93 | skateboarding/SkateBoarding-Front/010
94 | skateboarding/SkateBoarding-Front/011
95 | skateboarding/SkateBoarding-Front/012
96 | swinging/Swing-Bench/001
97 | swinging/Swing-Bench/002
98 | swinging/Swing-Bench/003
99 | swinging/Swing-Bench/004
100 | swinging/Swing-Bench/005
101 | swinging/Swing-Bench/006
102 | swinging/Swing-Bench/007
103 | swinging/Swing-Bench/008
104 | swinging/Swing-Bench/009
105 | swinging/Swing-Bench/010
106 | swinging/Swing-Bench/011
107 | swinging/Swing-Bench/012
108 | swinging/Swing-Bench/013
109 | swinging/Swing-Bench/014
110 | swinging/Swing-Bench/015
111 | swinging/Swing-Bench/016
112 | swinging/Swing-Bench/017
113 | swinging/Swing-Bench/018
114 | swinging/Swing-Bench/019
115 | swinging/Swing-Bench/020
116 | swinging-bar/Swing-SideAngle/001
117 | swinging-bar/Swing-SideAngle/002
118 | swinging-bar/Swing-SideAngle/003
119 | swinging-bar/Swing-SideAngle/004
120 | swinging-bar/Swing-SideAngle/005
121 | swinging-bar/Swing-SideAngle/006
122 | swinging-bar/Swing-SideAngle/007
123 | swinging-bar/Swing-SideAngle/008
124 | swinging-bar/Swing-SideAngle/009
125 | swinging-bar/Swing-SideAngle/010
126 | swinging-bar/Swing-SideAngle/011
127 | swinging-bar/Swing-SideAngle/012
128 | swinging-bar/Swing-SideAngle/013
129 | walking/Walk-Front/001
130 | walking/Walk-Front/002
131 | walking/Walk-Front/003
132 | walking/Walk-Front/004
133 | walking/Walk-Front/005
134 | walking/Walk-Front/006
135 | walking/Walk-Front/007
136 | walking/Walk-Front/008
137 | walking/Walk-Front/009
138 | walking/Walk-Front/010
139 | walking/Walk-Front/011
140 | walking/Walk-Front/012
141 | walking/Walk-Front/013
142 | walking/Walk-Front/014
143 | walking/Walk-Front/015
144 | walking/Walk-Front/016
145 | walking/Walk-Front/017
146 | walking/Walk-Front/018
147 | walking/Walk-Front/019
148 | walking/Walk-Front/020
149 | walking/Walk-Front/021
150 | walking/Walk-Front/022
151 |
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/data/ucf-sports/annotations/test_split.txt:
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1 | 1 2 3 4 15 16 17 18 19 20 33 34 35 36 37 38 53 54 59 60 61 62 71 72 73 74 84 85 86 87 96 97 98 99 100 101 116 117 118 119 129 130 131 132 133 134 135
2 |
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/data/ucf-sports/annotations/train_split.txt:
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1 | 5 6 7 8 9 10 11 12 13 14 21 22 23 24 25 26 27 28 29 30 31 32 39 40 41 42 43 44 45 46 47 48 49 50 51 52 55 56 57 58 63 64 65 66 67 68 69 70 75 76 77 78 79 80 81 82 83 88 89 90 91 92 93 94 95 102 103 104 105 106 107 108 109 110 111 112 113 114 115 120 121 122 123 124 125 126 127 128 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150
2 |
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/data/ucf-sports/wtv/sports-wtv.npy:
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https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/ucf-sports/wtv/sports-wtv.npy
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/data/visualization-example.png:
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https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/data/visualization-example.png
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/helper.py:
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1 | #
2 | # Helper functions for boxes.
3 | #
4 | # Categories of helper functions:
5 | # 1. Overlap functions.
6 | # 2. Spatial-aware object embedding functions.
7 | # 3. Misc.
8 | #
9 |
10 | import numpy as np
11 | from scipy.stats import entropy
12 |
13 | from sklearn.metrics import average_precision_score
14 |
15 | #
16 | # Helper functions category 1: Overlap functions.
17 | #
18 |
19 | #
20 | # 1.1
21 | # Compute the intersection between two boxes.
22 | #
23 | def boxintersect(a,b):
24 | if a[0] > b[2] or b[0] > a[2] or a[1] > b[3] or b[1] > a[3]:
25 | return 0
26 | w = min(a[2], b[2]) - max(a[0], b[0])
27 | h = min(a[3], b[3]) - max(a[1], b[1])
28 | return w * h
29 |
30 | #
31 | # 1.2
32 | # Overlap between two boxes.
33 | #
34 | def box_overlap(a, b):
35 | if a[2] > b[0] and b[2] > a[0] and a[3] > b[1] and b[3] > a[1]:
36 | i = min(a[2],b[2]) - max(a[0],b[0])
37 | i *= min(a[3],b[3]) - max(a[1],b[1])
38 | i = float(i)
39 | a1 = ((a[2]-a[0]) * (a[3]-a[1]))
40 | a2 = ((b[2]-b[0]) * (b[3]-b[1]))
41 | return i / (a1 + a2 - i)
42 | return 0.
43 |
44 | #
45 | # 1.3
46 | # Overlap between box and list of other boxes.
47 | #
48 | def liou(a, b):
49 | iou = np.zeros(b.shape[0])
50 | for i in xrange(b.shape[0]):
51 | iou[i] = box_overlap(a, b[i])
52 | return iou
53 |
54 | #
55 | # 1.4
56 | # Compute the intersection-over-union score for two proposals from the
57 | # same video.
58 | # Optional parameter: ss (stride for first tube, in case of sparse annotation
59 | # for second tube.
60 | #
61 | def tube_iou(p1, p2, ss=1):
62 | # Frame indices.
63 | p2idxs = np.where(p2[:,2] >= 0)[0]
64 | p1 = p1[::ss,:]
65 | p1f, p2f = p1[:,0].astype(int), p2[:,0].astype(int)
66 | p2f = p2f[p2idxs]
67 | p2 = p2[p2idxs,:]
68 |
69 | # Determine union of frame span.
70 | tmin = min(np.min(p1f), np.min(p2f))
71 | tmax = max(np.max(p1f), np.max(p2f))
72 |
73 | # Initialize the overlap scores across frame span.
74 | span = np.arange(tmin, tmax+1, ss)
75 | overlaps = np.zeros(len(span), dtype=np.float)
76 |
77 | # Go through the frame span.
78 | for d in xrange(len(span)):
79 | i = span[d]
80 | p1i, p2i = np.where(p1f == i)[0], np.where(p2f == i)[0]
81 | # Compute the overlap if frame in both proposals.
82 | if len(p1i) == 1 and len(p2i) == 1:
83 | a,b = p1[p1i[0],1:], p2[p2i[0],1:]
84 | a = [min(a[0],a[2]), min(a[1],a[3]), max(a[0],a[2]), max(a[1],a[3])]
85 | b = [min(b[0],b[2]), min(b[1],b[3]), max(b[0],b[2]), max(b[1],b[3])]
86 | # Only compute overlap if there is any
87 | if a[2] > b[0] and b[2] > a[0] and a[3] > b[1] and b[3] > a[1]:
88 | intersection = (min(a[2],b[2]) - max(a[0],b[0]))
89 | intersection *= (min(a[3],b[3]) - max(a[1],b[1]))
90 | intersection = float(intersection)
91 | area1 = ((a[2]-a[0]) * (a[3]-a[1]))
92 | area2 = ((b[2]-b[0]) * (b[3]-b[1]))
93 | overlaps[d] = intersection / (area1 + area2 - intersection)
94 |
95 | # Return the mean overlap over the frame span
96 | return np.mean(overlaps)
97 |
98 | #
99 | # Helper functions category 2: Embedding functions.
100 | #
101 |
102 | #
103 | # 2.1
104 | # Minimal edge distance between two boxes.
105 | #
106 | def box_dist(a, b):
107 | if boxintersect(a,b) > 0:
108 | return 0
109 | ae = np.array([[a[0],a[1]], [a[2],a[1]], [a[0],a[3]], [a[2],a[3]]])
110 | be = np.array([[b[0],b[1]], [b[2],b[1]], [b[0],b[3]], [b[2],b[3]]])
111 | mind = np.min(np.linalg.norm(ae-be[0], axis=1))
112 | for i in xrange(1, be.shape[0]):
113 | nd = np.min(np.linalg.norm(ae-be[i], axis=1))
114 | mind = min(mind, nd)
115 | return mind
116 |
117 | #
118 | # 2.2
119 | # Tile distributions with 9 tiles. a=person, b=object.
120 | #
121 | def tiledist(a, b):
122 | d = np.zeros(9)
123 | e = 1e6
124 | # Above left.
125 | d[0] = boxintersect([0, 0, a[0], a[1]], b)
126 | # Above center.
127 | d[1] = boxintersect([a[0], 0, a[2], a[1]], b)
128 | # Above right.
129 | d[2] = boxintersect([a[2], 0, e, a[1]], b)
130 | # Left.
131 | d[3] = boxintersect([0, a[1], a[0], a[3]], b)
132 | # On.
133 | d[4] = boxintersect(a, b)
134 | # Right.
135 | d[5] = boxintersect([a[2], a[1], e, a[3]], b)
136 | # Below left.
137 | d[6] = boxintersect([0, a[3], a[0], e], b)
138 | # Below center.
139 | d[7] = boxintersect([a[0], a[3], a[2], e], b)
140 | # Below right.
141 | d[8] = boxintersect([a[2], a[3], e, e], b)
142 | return d / float((b[2] - b[0]) * (b[3] - b[1]))
143 |
144 | #
145 | # 2.3
146 | # Find pairs of high scoring and high overlapping boxes for Viterbi.
147 | #
148 | def viterbi_scores(b1, s1, b2, s2, iouw):
149 | scores = np.zeros((s1.shape[0], s2.shape[0]))
150 | for i in xrange(s1.shape[0]):
151 | iou = liou(b1[i], b2)
152 | scores[i] = s1[i] + s2 + iouw * iou
153 | return scores
154 |
155 | #
156 | # Helper functions category 3: Misc functions.
157 | #
158 |
159 | #
160 | # 3.1
161 | # Jensen-Shannon Divergence.
162 | #
163 | def jensen_shannon_divergence(p, q):
164 | apq = 0.5 * (p + q)
165 | return 0.5 * entropy(p, apq, 2) + 0.5 * entropy(q, apq, 2)
166 |
167 | #
168 | # 3.2
169 | # Remove elements from a tube that correspond to non-annotations.
170 | # Used for experiments on Hollywood2Tubes, where the lack of action is
171 | # annotated with -1 values for the coordindates in the frame.
172 | #
173 | def tube_trim(tube):
174 | keep = np.where(tube[:,1] >= 0)[0]
175 | return tube[keep,:]
176 |
177 | #
178 | # 3.3
179 | # Interpolate a tube (done e.g. for UCF-101 due to its many videos).
180 | #
181 | def tube_interpolate(tube, scores, stride, nr_frames):
182 | if tube.shape[0] == nr_frames:
183 | return tube
184 |
185 | ntube = np.zeros((nr_frames, tube.shape[1]), dtype=tube.dtype)
186 | nscores = np.zeros(nr_frames)
187 |
188 | for i in xrange(nr_frames):
189 | i1, i2 = i / stride, i / stride + 1
190 | w = (i % stride) / float(stride)
191 |
192 | ntube[i,0] = i
193 | if i2 < tube.shape[0]:
194 | ntube[i,1] = (1-w) * tube[i1,1] + w * tube[i2,1]
195 | ntube[i,2] = (1-w) * tube[i1,2] + w * tube[i2,2]
196 | ntube[i,3] = (1-w) * tube[i1,3] + w * tube[i2,3]
197 | ntube[i,4] = (1-w) * tube[i1,4] + w * tube[i2,4]
198 | nscores[i] = (1-w) * scores[i1] + w * nscores[i2]
199 | else:
200 | ntube[i,1] = tube[i1,1]
201 | ntube[i,2] = tube[i1,2]
202 | ntube[i,3] = tube[i1,3]
203 | ntube[i,4] = tube[i1,4]
204 | nscores[i] = scores[i1]
205 |
206 | return ntube, nscores
207 |
208 |
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/local_detect.py:
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1 | #
2 | # Extract the boxes and scores for the objects from a Faster R-CNN model,
3 | # applied to a complete dataset.
4 | #
5 | # This is step 1 towards zero-shot action localization with spatial-aware
6 | # object embeddings.
7 | #
8 | # Pascal Mettes (2017).
9 | #
10 | # Please cite accordingly when using this code:
11 | #
12 | # @article{mettes2017spatial,
13 | # title={Spatial-Aware Object Embeddings for Zero-Shot Localization and
14 | # Classification of Actions},
15 | # author={Mettes, Pascal and Snoek, Cees G M},
16 | # journal={ICCV},
17 | # year={2017}
18 | #}
19 | #
20 |
21 | import os
22 | import sys
23 | import cv2
24 | import h5py
25 | import numpy as np
26 | import scipy.io as sio
27 | import argparse
28 | from ConfigParser import SafeConfigParser
29 |
30 | import caffe
31 | import _init_paths
32 | from fast_rcnn.config import cfg
33 | from fast_rcnn.test import im_detect
34 | from fast_rcnn.nms_wrapper import nms
35 | from utils.timer import Timer
36 |
37 | #
38 | # Parse all arguments.
39 | #
40 | def parse_args():
41 | parser = argparse.ArgumentParser(description="Detect local objects for a complete action dataset")
42 | parser.add_argument("-c", dest="configfile", help="Configuration file", default="config/ucf-sports.config", type=str)
43 | parser.add_argument("-s", dest="stride", help="Frame stride for object detection", default=1, type=int)
44 | parser.add_argument("-p", dest="prototxt", help="Location for Caffe prototxt (Faster R-CNN)", default="", type=str)
45 | parser.add_argument("-m", dest="caffemodel", help="Location for Caffe model (Faster R-CNN)", default="", type=str)
46 | args = parser.parse_args()
47 | return args
48 |
49 | #
50 | # Main entry point fo the script. In here: go through all videos and frames.
51 | #
52 | if __name__ == "__main__":
53 | # User parameters.
54 | args = parse_args()
55 | config = args.configfile
56 | stride = args.stride
57 | prototxt = args.prototxt
58 | caffemodel = args.caffemodel
59 |
60 | # Yield directories and videos.
61 | parser = SafeConfigParser()
62 | parser.read(config)
63 | framedir = parser.get('actions', 'framedir')
64 | resdir = parser.get('actions', 'scoredir')
65 | vidlist = parser.get('actions', 'vidfile')
66 | videos = np.array([v.strip().split()[0] for v in open(vidlist)])
67 |
68 | # Caffe / Faster R-CNN settings.
69 | ### Use region proposals.
70 | cfg.TEST.HAS_RPN = True
71 | ### Set to CPU/GPU.
72 | caffe.set_mode_gpu()
73 |
74 | # Load the network.
75 | net = caffe.Net(prototxt, caffemodel, caffe.TEST)
76 | print "HERE"
77 |
78 | # Go over all videos and all frames.
79 | for video in videos:
80 | print video
81 | frames = sorted(os.listdir(framedir + video))
82 |
83 | # Create resulting directory if not existing already.
84 | if not os.path.exists(resdir + video):
85 | os.makedirs(resdir + video)
86 |
87 | # Go over the frames.
88 | for i in xrange(0,len(frames),stride):
89 | print i,
90 | sys.stdout.flush()
91 |
92 | # Skip if done before.
93 | if os.path.exists(resdir + video + "/" + frames[i][:-3] + "npy"):
94 | continue
95 |
96 | # Load frame, get data, stack data, store data.
97 | try:
98 | frame = cv2.imread(basedir + video + "/" + frames[i])
99 | s,b = im_detect(net, frame)
100 | t = np.hstack((s,b))
101 | np.save(resdir + video + "/" + frames[i][:-3] + "npy", t)
102 | except:
103 | pass
104 | print
105 |
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/local_link.py:
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1 | #
2 | # Use the box scores of individual frames to generate action tubes.
3 | #
4 | # This is step 3 towards zero-shot action localization with spatial-aware
5 | # object embeddings.
6 | #
7 | # Pascal Mettes (2017).
8 | #
9 | # Please cite accordingly when using this code:
10 | #
11 | # @article{mettes2017spatial,
12 | # title={Spatial-Aware Object Embeddings for Zero-Shot Localization and
13 | # Classification of Actions},
14 | # author={Mettes, Pascal and Snoek, Cees G M},
15 | # journal={ICCV},
16 | # year={2017}
17 | #}
18 | #
19 |
20 | import os
21 | import sys
22 | import h5py
23 | import numpy as np
24 | import argparse
25 |
26 | import helper
27 |
28 | from ConfigParser import SafeConfigParser
29 |
30 | #
31 | # Parse all arguments.
32 | #
33 | def parse_args():
34 | parser = argparse.ArgumentParser(description="Detect local objects for a complete action dataset")
35 | parser.add_argument("-c", dest="configfile", help="Configuration file", default="config/ucf-sports.config", type=str)
36 | parser.add_argument("-b", dest="basedir", help="Directory where data from step 2 is stored", default="", type=str)
37 | parser.add_argument("-i", dest="iou_weight", help="Weight for the overlap score when linking boxes", default=1.0, type=float)
38 | parser.add_argument("-s", dest="stride", help="Stride between frames when computing scores (for interpolation)", default=1, type=int)
39 | parser.add_argument("-t", dest="nr_tubes", help="Number of tubes per action per video", default=5, type=int)
40 | args = parser.parse_args()
41 | return args
42 |
43 | #
44 | # Generate tubes by linking high scoring spatial-aware object embedding boxes.
45 | #
46 | class GenerateTubes(object):
47 |
48 | #
49 | # Initialize the class by settng the paths and loading the videos.
50 | #
51 | def __init__(self, configfile, basedir):
52 | # Parse the configurationfile.
53 | parser = SafeConfigParser()
54 | parser.read(configfile)
55 | self.dataset = configfile.split("/")[1].split(".")[0]
56 |
57 | # Yield the appropriate directories.
58 | self.gtdir = parser.get('actions', 'gtdir')
59 | self.domdir = parser.get('actions', 'domdir')
60 | self.scoredir = parser.get('actions', 'scoredir')
61 | self.tubedir = parser.get('actions', 'tubedir')
62 | self.framedir = parser.get('actions', 'framedir')
63 | self.wtvfile = parser.get('actions', 'wtvfile')
64 | self.split = parser.get('actions', 'testsplit')
65 | self.vidlist = parser.get('actions', 'vidfile')
66 |
67 | self.basedir = basedir
68 |
69 | # Load the split indices and set the training videos.
70 | self.teidxs = np.loadtxt(self.split, dtype=int) - 1
71 | self.videos = [line.strip().split()[0] for line in open(self.vidlist)]
72 | self.videos = np.array(self.videos)
73 | self.videos = self.videos[self.teidxs]
74 |
75 | # Yield action names.
76 | self.actions = np.array([vid.split("/")[0] for vid in self.videos])
77 | self.actions, self.aidxs = np.unique(self.actions, return_inverse=True)
78 |
79 | #
80 | # Generate the tubes from scores.
81 | #
82 | def generate(self, nr_tubes, iou_weight, stride):
83 | # Do the scoring for each action for each video.
84 | for i in xrange(len(self.actions)):
85 | print "Action %d/%d: %s" %(i+1, len(self.actions), self.actions[i])
86 |
87 | # Go through all videos.
88 | for j in xrange(len(self.videos)):
89 | print "\tVideo %d/%d\r" %(j+1, len(self.videos)),
90 | sys.stdout.flush()
91 |
92 | # Initialize the path wherr the tubes will be stored.
93 | bsplit = self.basedir.split("/")
94 | h5d = self.tubedir + "%s/%s/nrtubes-%d_iouw-%.2f/%s/%s/" \
95 | %(bsplit[-3], bsplit[-2], nr_tubes, iou_weight, \
96 | self.actions[i], self.videos[j])
97 | h5f = h5d + "/tubes.hdf5"
98 | if os.path.exists(h5f):
99 | continue
100 | if not os.path.exists(h5d):
101 | os.makedirs(h5d)
102 |
103 | # Yield frames.
104 | bdir = self.basedir + self.actions[i] + "/" + self.videos[j]
105 | frames = sorted(os.listdir(bdir))
106 | framescores = []
107 | frameboxes = []
108 | print bdir
109 |
110 | # Number of frames (for interpolation).
111 | if self.dataset == "h2t":
112 | flist = os.listdir(self.framedir + self.videos[j].split("/")[1])
113 | nrframes = len(flist)
114 | elif self.dataset == "ucf-101":
115 | flist = os.listdir(self.framedir + self.videos[j].split("/")[1][:-4])
116 | nrframes = len(flist)
117 | else:
118 | nrframes = len(frames)
119 |
120 | # Go over all frames.
121 | for k in xrange(len(frames)):
122 | framedata = np.load(bdir + "/" + frames[k])
123 | framescores.append(framedata['scores'])
124 | frameboxes.append(framedata['boxes'])
125 |
126 | # Open the file where to save the tubes.
127 | h5f = h5py.File(h5f, "w")
128 |
129 | # Generate the tubes.
130 | for k in xrange(nr_tubes):
131 | ds, di = [], []
132 | print k, nr_tubes
133 |
134 | # Initialize.
135 | for l in xrange(len(frameboxes)):
136 | frsize = frameboxes[l].shape[0]
137 | ds.append(np.zeros(frsize))
138 | di.append(np.zeros(frsize, dtype=int))
139 |
140 | # Viterbi.
141 | for l in xrange(len(frameboxes)-2, -1, -1):
142 | cscores = helper.viterbi_scores(frameboxes[l], \
143 | framescores[l], frameboxes[l+1], \
144 | framescores[l+1], iou_weight)
145 | cscores += ds[l+1]
146 | ds[l] = np.max(cscores, 1)
147 | di[l] = np.argmax(cscores, 1)
148 |
149 | # Start from frame 0.
150 | sf, si = 0, np.argmax(ds[0])
151 | tube = [frameboxes[sf][si]]
152 | tscore = [framescores[sf][si]]
153 | tubeidxs = [sf*stride]
154 | idx = si
155 | framescores[sf][si] = 0
156 |
157 | # Connect.
158 | for l in xrange(sf, len(frameboxes)-1):
159 | idx = di[l][idx]
160 | tube.append(frameboxes[l+1][idx])
161 | tscore.append(framescores[l+1][idx])
162 | tubeidxs.append((l+1)*stride)
163 | framescores[l+1][idx] = 0
164 |
165 | # Numpy format and save.
166 | tube = np.vstack(tube)
167 | tscore = np.array(tscore)
168 | tubeidxs = np.array(tubeidxs)
169 | tube = np.hstack((tubeidxs[:,np.newaxis], tube))
170 |
171 | # Interpolation.
172 | if stride > 1:
173 | tube, tscore = helper.tube_interpolate(tube, tscore, stride, nrframes)
174 |
175 | d1 = h5f.create_dataset(str(k), tube.shape)
176 | d1[:,:] = tube.copy()
177 | d2 = h5f.create_dataset("scores-%d" %(k), tscore.shape)
178 | d2[:] = tscore.copy()
179 |
180 | # Close the file.
181 | h5f.close()
182 | print
183 |
184 | #
185 | # Entry point of the script.
186 | #
187 | if __name__ == "__main__":
188 | # User parameters.
189 | args = parse_args()
190 |
191 | # Specify which dataset to use.
192 | configfile = args.configfile
193 |
194 | # Hyperparameters.
195 | nr_tubes = args.nr_tubes
196 | basedir = args.basedir
197 | iou_weight = args.iou_weight
198 | stride = args.stride
199 |
200 | tg = GenerateTubes(args.configfile, args.basedir)
201 | tg.generate(args.nr_tubes, args.iou_weight, args.stride)
202 |
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/local_score.py:
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1 | #
2 | # Score the boxes based on person detection and interaction with dominant
3 | # objects.
4 | #
5 | # This is step 2 towards zero-shot action localization with spatial-aware
6 | # object embeddings.
7 | #
8 | # Pascal Mettes (2017).
9 | #
10 | # Please cite accordingly when using this code:
11 | #
12 | # @article{mettes2017spatial,
13 | # title={Spatial-Aware Object Embeddings for Zero-Shot Localization and
14 | # Classification of Actions},
15 | # author={Mettes, Pascal and Snoek, Cees G M},
16 | # journal={ICCV},
17 | # year={2017}
18 | #}
19 | #
20 |
21 | import os
22 | import sys
23 | import h5py
24 | import numpy as np
25 | import argparse
26 |
27 | import _init_paths
28 | from fast_rcnn.nms_wrapper import nms
29 |
30 | import helper
31 | import word_to_vec
32 |
33 | from ConfigParser import SafeConfigParser
34 |
35 | #
36 | # Parse all arguments.
37 | #
38 | def parse_args():
39 | parser = argparse.ArgumentParser(description="Detect local objects for a complete action dataset")
40 | parser.add_argument("-c", dest="configfile", help="Configuration file", default="config/ucf-sports.config", type=str)
41 | parser.add_argument("-t", dest="topn", help="Number of objects to used per action", default=1, type=int)
42 | parser.add_argument("-d", dest="bdist", help="Maximum allowed distance between actors and objects (border distance in pixels)", default=25, type=int)
43 | parser.add_argument("-n", dest="nmst", help="Threshold for non-maximum suppression to score less boxes", default=0.5, type=int)
44 | parser.add_argument("-w", dest="ow", help="Relative weight of the object score compared to actor scores", default=1.0, type=float)
45 | args = parser.parse_args()
46 | return args
47 |
48 | #
49 | # Reweight scores based on person scores and relations to objects.
50 | #
51 | def frame_scores(pdata, odata, priors, oweights, ow, bdist):
52 | scores = pdata[:,-1].copy()
53 | for i in xrange(pdata.shape[0]):
54 | for j in xrange(len(odata)):
55 | bs, bi = -1, -1
56 | for k in xrange(odata[j].shape[0]):
57 | if odata[j][k,-1] > bs and helper.box_dist(pdata[i,:4], \
58 | odata[j][k,:4]) <= bdist:
59 | bs, bi = odata[j][k,-1], k
60 | distribution = helper.tiledist(odata[j][bi,:4], pdata[i,:4])
61 | scores[i] += ow * bs * oweights[j] * (1 - \
62 | helper.jensen_shannon_divergence(priors[j], distribution))
63 | return scores
64 |
65 | #
66 | # Main object for scoring boxes in video frames based on actors, objects,
67 | # and spatial relations between them.
68 | #
69 | class ScoreBoxes(object):
70 |
71 | #
72 | # Initialize the class by reading the configfile, yielding the videos,
73 | # and loading the word2vec data + spatial relations.
74 | #
75 | def __init__(self, configfile):
76 | # Parse the configurationfile.
77 | parser = SafeConfigParser()
78 | parser.read(configfile)
79 | self.dataset = configfile.split("/")[1].split(".")[0]
80 |
81 | # Yield the appropriate directories.
82 | self.domdir = parser.get('actions', 'domdir')
83 | self.scoredir = parser.get('actions', 'scoredir')
84 | self.wtvfile = parser.get('actions', 'wtvfile')
85 | self.split = parser.get('actions', 'testsplit')
86 | self.vidlist = parser.get('actions', 'vidfile')
87 |
88 | # Load the split indices and set the training videos.
89 | self.teidxs = np.loadtxt(self.split, dtype=int) - 1
90 | self.videos = [line.strip().split()[0] for line in open(self.vidlist)]
91 | self.videos = np.array(self.videos)
92 | self.videos = self.videos[self.teidxs]
93 |
94 | # Yield action names.
95 | self.actions = np.array([vid.split("/")[0] for vid in self.videos])
96 | self.actions, self.aidxs = np.unique(self.actions, return_inverse=True)
97 |
98 | # Load the wordtovec file for the actions.
99 | self.wtva = np.load(self.wtvfile)
100 |
101 | # Actor indices (box coordinates and scores).
102 | self.pidxs = np.array([85,86,87,88,1])
103 |
104 | # Load the prior spatial distributions from file.
105 | self.priors = np.load("data/mscoco/mscoco_priors_new.npy")
106 | self.priors = self.priors.reshape(-1)[0]
107 |
108 | #
109 | # For each action in the dataset, find the top n most relevant objects.
110 | # Relevancy is determined through word2vec.
111 | #
112 | def select_local_objects(self, topn):
113 | # Store for logging purposes.
114 | self.topn = topn
115 |
116 | # MS-COCO class names and vectors.
117 | self.objectnames = open("data/mscoco/mscoco_classes.txt").readlines()
118 | self.objectnames = np.array([on.strip() for on in self.objectnames])
119 | wtvo = np.load("data/mscoco/mscoco-wtv.npy")
120 |
121 | # Find the top dominant objects per action.
122 | self.action_to_object_i = []
123 | self.action_to_object_w = []
124 | for i in xrange(len(self.actions)):
125 | ai, aw = word_to_vec.topn(self.wtva[i], wtvo, topn)
126 | self.action_to_object_i.append(ai+1)
127 | self.action_to_object_w.append(aw)
128 |
129 | #
130 | # Perform the actual scoring in here.
131 | # Score each box in each video frame.
132 | #
133 | def score(self, bdist, nmst, ow):
134 | # Do the scoring for each action for each video.
135 | for i in xrange(len(self.actions)):
136 | print "Action %d/%d: %s" %(i+1, len(self.actions), self.actions[i])
137 |
138 | # Yield the object indices for the score and box extraction.
139 | oidxs = []
140 | for j in xrange(len(self.action_to_object_i[i])):
141 | oidxs.append(list(range(self.action_to_object_i[i][j]*4+81, \
142 | (self.action_to_object_i[i][j]+1)*4+81)) + \
143 | [self.action_to_object_i[i][j]])
144 |
145 | # Yield the corresponding priors (spatial relations).
146 | opriors = []
147 | for j in xrange(len(self.action_to_object_i[i])):
148 | opriors.append(self.priors[self.objectnames[self.action_to_object_i[i][j]-1]])
149 |
150 | # Go over all videos.
151 | for j in xrange(len(self.videos)):
152 | print "\tVideo %d/%d\r" %(j+1, len(self.videos)),
153 | sys.stdout.flush()
154 | print
155 |
156 | # Generate result directory.
157 | rdir = self.scoredir + "topn-%d/ow-%.2f_bdist-%d_nms-%.3f/%s/%s/" \
158 | %(self.topn, ow, bdist, nmst, self.actions[i], self.videos[j])
159 | if not os.path.exists(rdir):
160 | os.makedirs(rdir)
161 |
162 | # Yield frames.
163 | if self.dataset == "ucf-sports":
164 | ddir = self.domdir + self.videos[j]
165 | elif self.dataset == "j-hmdb":
166 | ddir = self.domdir + self.videos[j] + ".avi"
167 | elif self.dataset == "h2t":
168 | ddir = self.domdir + self.videos[j].split("/")[1]
169 | elif self.dataset == "ucf-101":
170 | ddir = self.domdir + self.videos[j].split("/")[1][:-4]
171 | frames = sorted(os.listdir(ddir))
172 |
173 | # Go over all frames.
174 | for k in xrange(len(frames)):
175 | # Skip done work.
176 | if os.path.exists(rdir + "frame-%05d-data.npz" %(k)):
177 | continue
178 |
179 | # Load the boxes and scores.
180 | framedata = np.load(ddir + "/" + frames[k])
181 |
182 | # Person boxes and scores.
183 | persondata = framedata[:,self.pidxs]
184 | # Reduce computational effort with non-maximum suppression.
185 | pkeep = nms(persondata, nmst)
186 | persondata = persondata[pkeep,:]
187 |
188 | # Compute scores.
189 | if self.topn == 0:
190 | # Only use actor (baseline in the paper).
191 | newscores = persondata[:,-1].copy()
192 | else:
193 | # Object boxes and scores.
194 | objectdata = []
195 | for l in xrange(len(oidxs)):
196 | od = framedata[:,oidxs[l]]
197 | okeep = nms(od, nmst)
198 | od = od[okeep,:]
199 | objectdata.append(od)
200 |
201 | # Score per box.
202 | newscores = frame_scores(persondata, objectdata, \
203 | opriors, self.action_to_object_w[i], ow, bdist)
204 |
205 | # Store the indices, boxes, and scores.
206 | np.savez(rdir + "frame-%05d-data.npz" %(k), boxes=persondata[:,:4], \
207 | boxidxs=pkeep, scores=newscores)
208 | print
209 | #
210 | # Entry point of the script.
211 | #
212 | if __name__ == "__main__":
213 | # User parameters.
214 | args = parse_args()
215 |
216 | # Specify which dataset to use.
217 | configfile = args.configfile
218 |
219 | # Hyperparameters.
220 | topn = args.topn
221 | bdist = args.bdist
222 | nmst = args.nmst
223 | ow = args.ow
224 |
225 | # Initialize and run the box scoring.
226 | boxmodel = ScoreBoxes(configfile)
227 | boxmodel.select_local_objects(topn)
228 | boxmodel.score(bdist, nmst, ow)
229 |
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/visualize/example/frames/0055.jpg:
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https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/visualize/example/frames/0055.jpg
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/visualize/example/tube.hdf5:
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https://raw.githubusercontent.com/psmmettes/spatial-aware-object-embeddings/ba7084de9437cbbc49caf6ac1e2da234a1a0cb38/visualize/example/tube.hdf5
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/visualize/visualize_video_tube.py:
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1 | #
2 | # This script visualizes a set of 3D tubes through a video.
3 | #
4 | # Written by: Pascal Mettes.
5 | # Original code: Jan van Gemert.
6 | #
7 | # 2017
8 | #
9 |
10 | import os
11 | import sys
12 | import glob
13 | import h5py
14 | from PIL import Image
15 | import numpy as np
16 | import mayavi
17 | from mayavi import mlab
18 | from tvtk.api import tvtk
19 | from tvtk.common import configure_input
20 |
21 | #
22 | # Load a set of figures from a directory.
23 | #
24 | # Input:
25 | # framedir (str) - Directory containing the frames.
26 | # ext (str) - Frame file extension.
27 | #
28 | # Output:
29 | # 4D tensor (nrf, nry, nrx, nrc).
30 | #
31 | def load_frames(framedir, ext="jpg"):
32 | # Load the sort the frames.
33 | frames = os.path.join(framedir, "*." + ext)
34 | frames = glob.glob(frames)
35 | frames.sort()
36 |
37 | # Load frame info.
38 | data = []
39 | for i in xrange(len(frames)):
40 | frame = np.array(Image.open(frames[i]))
41 | data.append(frame)
42 | data = np.array(data)
43 |
44 | return data
45 |
46 | #
47 | # Convert frame data to an actor for 3D visualization.
48 | #
49 | # Input:
50 | # frame (numpy ndarray) - Frame data.
51 | # width (int) - Width.
52 | # height (int) - Height.
53 | # b (int) - Border.
54 | #
55 | # Output:
56 | # Tvtk actor of the frame.
57 | #
58 | def frame_to_actor(frame, width, height, b=5):
59 | # Dimensions with borders.
60 | w, h = width+2*b, height+2*b
61 |
62 | # Initialize array.
63 | data = np.zeros((w, h, 4))
64 | if frame is None:
65 | data[:,:,-1] = 255
66 | data[b:-b,b:-b,-1] = 0
67 | else:
68 | data[b:-b,b:-b,:3] = frame
69 | data[:,:,-1] = 255
70 | data = np.transpose(data, (1,0,2))
71 | colors = np.reshape(data, (w*h,4))
72 |
73 | # Create actor.
74 | image = tvtk.ImageData()
75 | image.point_data.scalars=tvtk.UnsignedCharArray()
76 | image.point_data.scalars.from_array(colors)
77 | image.dimensions = np.array((w, h, 1))
78 | actor = tvtk.ImageActor()
79 | configure_input(actor, image)
80 |
81 | return actor
82 |
83 | #
84 | # Visualize an individual tube through the video.
85 | #
86 | # Input:
87 | # tube (numpy ndarray) - Tube content.
88 | # color (rgb tuple) - Tube color.
89 | # width int) - Width.
90 | # height (int) - Height.
91 | # nr_frames (int) - Number of frames in whole video.
92 | # extent (float) - Temporal scale factor.
93 | # b (int) - Border.
94 | #
95 | # Output:
96 | # -
97 | #
98 | def visualize_tube(tube, color, width, height, nr_frames, extent, b=5):
99 | # Fill a volume with tube data.
100 | cube = np.zeros((width, height, nr_frames))
101 | for i in xrange(tube.shape[0]):
102 | fidx = int(nr_frames - tube[i,0] - 1)
103 | box = tube[i,1:] + b
104 | box = box.astype(int)
105 | xmin, ymin, xmax, ymax = box
106 | cube[ymin:ymax,xmin:xmax, fidx] = 1.0
107 |
108 | # Create scalar field.
109 | field = mlab.pipeline.scalar_field(cube)
110 | # Re-adjust temporal scale.
111 | field.spacing = [1,1,extent]
112 | field.update_image_data = True
113 |
114 | # Plot the field.
115 | iso = mlab.pipeline.iso_surface(field, opacity=0.4, color=color)
116 | iso.scene.anti_aliasing_frames = 1
117 | iso.contour.number_of_contours = 2
118 |
119 | #
120 | # Visualize the 3D scene of a video and a set of colored tubes.
121 | #
122 | # Input:
123 | # frames (nrf, nrx, nry, nrc) - Frame data.
124 | # tubes (list) - List of tubes.
125 | # colors (list) - Color per tube.
126 | # extent (float) - Temporal scale.
127 | # nr_middle_frames (int) - Number of frames in the middle (border only).
128 | #
129 | # Output:
130 | # -
131 | #
132 | def visualize_scene(frames, tubes, colors, extent, nr_middle_frames=0):
133 | # Create the figure.
134 | figure = mlab.figure(fgcolor=(0,0,0), bgcolor=(1,1,1), size=(1600,900))
135 | figure.scene.anti_aliasing_frames = 1
136 |
137 | # Retrieve frame dimensions.
138 | [nrf, nrx, nry, nrc] = frames.shape
139 |
140 | # Set camera position.
141 | figure.scene.z_plus_view()
142 | mayavi.mlab.roll(-90)
143 | figure.scene.parallel_projection = True
144 | figure.scene.camera.azimuth(25)
145 | figure.scene.camera.elevation(30)
146 | figure.scene.camera.zoom(1.5)
147 |
148 | # Show end frame.
149 | endframe = frame_to_actor(frames[-1], nrx, nry)
150 | endframe.position = [0,0,0]
151 | figure.scene.add_actor(endframe)
152 |
153 | # Show optionaly intermediate frame positions with only black borders.
154 | fs = nrf*extent / (nr_middle_frames+1)
155 | for i in xrange(nr_middle_frames):
156 | frame = frame_to_actor(None, nrx, nry)
157 | frame.position = [0,0,fs*(i+1)]
158 | figure.scene.add_actor(frame)
159 |
160 | # Show tubes.
161 | for i in xrange(len(tubes)):
162 | visualize_tube(tubes[i], colors[i], nrx, nry, nrf, extent)
163 |
164 | # Show start frame.
165 | startframe = frame_to_actor(frames[0], nrx, nry)
166 | startframe.position = [0,0,nrf*extent]
167 | figure.scene.add_actor(startframe)
168 |
169 | # Show the result and write it to file.
170 | mlab.show()
171 |
172 | #
173 | # Run an example visualization when running the script directly.
174 | #
175 | if __name__ == "__main__":
176 | # Directory with frames.
177 | framedir = "example/frames/"
178 | # Load a tube.
179 | tubes = [np.array(h5py.File("example/tube.hdf5", "r")["0"])]
180 | # Color of the tube.
181 | colors = [(1,0,0)]
182 | # Temporal extent of the video.
183 | extent = 30
184 | # Number of support frames in the middle.
185 | nr_middle_frames = 1
186 |
187 | # Load the frames.
188 | frames = load_frames(framedir)
189 |
190 | # Visualize.
191 | visualize_scene(frames, tubes, colors, extent, nr_middle_frames)
192 |
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/word_to_vec.py:
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1 | #
2 | # Word2Vec vectors to distances.
3 | #
4 | # This file contains helper functions for selecting relevant objects from
5 | # arbitrary actions.
6 | #
7 |
8 | import numpy as np
9 | from scipy.spatial.distance import cosine
10 |
11 | #
12 | # Normalize word2vec vectors.
13 | #
14 | def normalize(a):
15 | for i in xrange(a.shape[0]):
16 | a[i] /= np.linalg.norm(a[i])
17 | return a
18 |
19 | #
20 | # Cosine similarity between vectors.
21 | #
22 | def cos_sim(a, b):
23 | return 1 - cosine(a, b)
24 |
25 | #
26 | # Select top n vectors from b given a vector a.
27 | #
28 | def topn(a, b, n):
29 | cs = np.zeros(b.shape[0])
30 | for i in xrange(b.shape[0]):
31 | cs[i] = cos_sim(a, b[i])
32 | co = np.argsort(cs)[::-1]
33 | ci = co[:n]
34 | cw = cs[co[:n]]
35 | # Normalize the scores to add to one per action, not used in paper.
36 | #if len(cw) > 0:
37 | # cw /= np.sum(cw)
38 | return ci, cw
39 |
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