├── README.md └── bibfiles ├── image-classification.bib ├── semi-supervised-semantic-segmentation.bib ├── human-detection.bib ├── 2d-semantic-segmentation.bib ├── image-level-weakly-supervised-segmentation.bib └── 2d-object-detection.bib /README.md: -------------------------------------------------------------------------------- 1 | # Awesome-CV-bibfiles 2 | A collection of bibfiles related to computer vision for efficiency 3 | 4 | ## Highlights 5 | - Bibfiles are organized by category 6 | - Short name of the method is used as the index of reference item such as `RCNN` rather than `girshick14CVPR` for efficient retrieval 7 | 8 | ## Contributing 9 | Please feel free to send [pull requests](https://github.com/hkzhang95/Awesome-CV-bibfiles/pulls). You can follow the [templates](#templates) of the reference item for more efficient cooperations. 10 | 11 | ## Table of Contents 12 | - [Image Classification](bibfiles/image-classification.bib) 13 | - [2D General Object Detection](bibfiles/2d-object-detection.bib) 14 | - [Human Detection](bibfiles/human-detection.bib) 15 | - [2D Semantic Segmentation](bibfiles/2d-semantic-segmentation.bib) 16 | - [Image Level Weakly Supervised Segmentation](bibfiles/image-level-weakly-supervised-segmentation.bib) 17 | 18 | ## Templates 19 | - Recommended indentation: **4 spaces** 20 | - Drop one line between reference items 21 | 22 | ### Conference paper 23 | ``` 24 | @inproceedings{index_name, 25 | author = {A and B and C}, 26 | title = {title}, 27 | booktitle = {abbreviation form of the conference, such as CVPR and ICCV}, 28 | year = {xxxx} 29 | } 30 | ``` 31 | 32 | Specially, for the Arxiv version: 33 | ``` 34 | @article{index_name, 35 | author = {A and B and C}, 36 | title = {title}, 37 | journal = {arXiv preprint arXiv:xxxx.xxxxx}, 38 | year = {xxxx} 39 | } 40 | ``` 41 | 42 | ### Journal paper 43 | ``` 44 | @article{index_name, 45 | author = {A and B and C}, 46 | title = {title}, 47 | volume = {volume}, 48 | number = {number}, 49 | pages = {start-end}, 50 | journal = {Abbreviation form of the journal, such as IJCV and T-PAMI}, 51 | year = {xxxx} 52 | } 53 | ``` 54 | -------------------------------------------------------------------------------- /bibfiles/image-classification.bib: -------------------------------------------------------------------------------- 1 | @article{LeNet, 2 | author = {Yann LeCun and Léon Bottou and Yoshua Bengio and Patrick Haffner}, 3 | title = {Gradient-Based Learning Applied to Document Recognition}, 4 | volume = {86}, 5 | number = {11}, 6 | pages = {2278-2324}, 7 | journal = {Proceedings of the IEEE}, 8 | year = {1998} 9 | } 10 | 11 | @inproceedings{HOG, 12 | author = {Dalal, Navneet and Triggs, Bill}, 13 | title = {Histograms of Oriented Gradients for Human Detection}, 14 | booktitle = {CVPR}, 15 | year = {2005} 16 | } 17 | 18 | @inproceedings{SURF, 19 | author = {Bay, Herbert and Tuytelaars, Tinne and Van Gool, Luc}, 20 | title = {{SURF}: Speeded Up Robust Features}, 21 | booktitle = {ECCV}, 22 | year = {2006} 23 | } 24 | 25 | @inproceedings{ImageNet, 26 | author = {Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, 27 | title = {{ImageNet}: A Large-Scale Hierarchical Image Database}, 28 | booktitle = {CVPR}, 29 | year = {2009} 30 | } 31 | 32 | @inproceedings{SIFT, 33 | author = {Lowe, David G}, 34 | title = {Object Recognition from Local Scale-Invariant Features}, 35 | booktitle = {ICCV}, 36 | year = {2009} 37 | } 38 | 39 | @inproceedings{AlexNet, 40 | author = {Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E}, 41 | title = {{ImageNet} Classification with Deep Convolutional Neural Networks}, 42 | booktitle = {NeurIPS}, 43 | year = {2012} 44 | } 45 | 46 | @inproceedings{VGG, 47 | author = {Simonyan, Karen and Zisserman, Andrew}, 48 | title = {Very Deep Convolutional Networks for Large-Scale Image Recognition}, 49 | booktitle = {ICLR}, 50 | year = {2015} 51 | } 52 | 53 | @inproceedings{BatchNorm, 54 | author = {Ioffe, Sergey and Szegedy, Christian}, 55 | title = {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift}, 56 | booktitle = {ICML}, 57 | year = {2015} 58 | } 59 | 60 | @inproceedings{GoogLeNet, 61 | author = {Christian Szegedy and Wei Liu and Yangqing Jia and Pierre Sermanet and Scott E. Reed and Dragomir Anguelov and Dumitru Erhan and Vincent Vanhoucke and Andrew Rabinovich}, 62 | title = {Going Deeper with Convolutions}, 63 | booktitle = {CVPR}, 64 | year = {2015} 65 | } 66 | 67 | @inproceedings{ResNet, 68 | author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, 69 | title = {Deep Residual Learning for Image Recognition}, 70 | booktitle = {CVPR}, 71 | year = {2016} 72 | } 73 | 74 | @inproceedings{DenseNet, 75 | author = {Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q}, 76 | title = {Densely Connected Convolutional Networks}, 77 | booktitle = {CVPR}, 78 | year = {2017} 79 | } 80 | 81 | @inproceedings{EfficientNet, 82 | author = {Mingxing Tan and Quoc V.Le}, 83 | title = {{EfficientNet}: Rethinking Model Scaling for Convolutional Neural Networks}, 84 | booktitle = {ICML}, 85 | year = {2019} 86 | } 87 | -------------------------------------------------------------------------------- /bibfiles/semi-supervised-semantic-segmentation.bib: -------------------------------------------------------------------------------- 1 | 2 | @inproceedings{soulyGAN, 3 | author = {Souly, Nasim and Spampinato, Concetto and Shah, Mubarak}, 4 | title = {Semi supervised semantic segmentation using generative adversarial network}, 5 | booktitle = {ICCV}, 6 | year = {2017} 7 | } 8 | 9 | @inproceedings{AdvSemiSeg, 10 | author = {Hung, Wei-Chih and Tsai, Yi-Hsuan and Liou, Yan-Ting and Lin, Yen-Yu and Yang, Ming-Hsuan}, 11 | title = {Adversarial learning for semi-supervised semantic segmentation}, 12 | booktitle = {BMVC}, 13 | year = {2018} 14 | } 15 | 16 | @article{S4GAN, 17 | author = {Mittal, Sudhanshu and Tatarchenko, Maxim and Brox, Thomas}, 18 | title = {Semi-supervised semantic segmentation with high-and low-level consistency}, 19 | journal = {T-PAMI}, 20 | year = {2019} 21 | } 22 | 23 | @inproceedings{self-correcting, 24 | author = {Ibrahim, Mostafa S and Vahdat, Arash and Ranjbar, Mani and Macready, William G}, 25 | title = {Semi-supervised semantic image segmentation with self-correcting networks}, 26 | booktitle = {CVPR}, 27 | year = {2020} 28 | } 29 | 30 | @inproceedings{CCT, 31 | author = {Ouali, Yassine and Hudelot, C{\'e}line and Tami, Myriam}, 32 | title = {Semi-supervised semantic segmentation with cross-consistency training}, 33 | booktitle = {CVPR}, 34 | year = {2020} 35 | } 36 | 37 | @inproceedings{PseudoSeg, 38 | author = {Zou, Yuliang and Zhang, Zizhao and Zhang, Han and Li, Chun-Liang and Bian, Xiao and Huang, Jia-Bin and Pfister, Tomas}, 39 | title = {PseudoSeg: Designing pseudo labels for semantic segmentation}, 40 | booktitle = {ICLR}, 41 | year = {2021} 42 | } 43 | 44 | @inproceedings{dual-branch, 45 | author = {Luo, Wenfeng and Yang, Meng}, 46 | title = {Semi-supervised semantic segmentation via strong-weak dual-branch network}, 47 | booktitle = {ECCV}, 48 | year = {2020} 49 | } 50 | 51 | @inproceedings{GCT, 52 | author = {Ke, Zhanghan and Qiu, Di and Li, Kaican and Yan, Qiong and Lau, Rynson W. H.}, 53 | title = {Guided collaborative training for pixel-wise semi-supervised learning}, 54 | booktitle = {ECCV}, 55 | year = {2020} 56 | } 57 | 58 | @inproceedings{ECS, 59 | author = {Mendel, Robert and de Souza, Luis Antonio and Rauber, David and Papa, Jo{\~a}o Paulo and Palm, Christoph}, 60 | title = {Semi-supervised segmentation based on error-correcting supervision}, 61 | booktitle = {ECCV}, 62 | year = {2020} 63 | } 64 | 65 | @article{simplebaseline, 66 | author = {Yuan, Jianlong and Liu, Yifan and Shen, Chunhua and Wang, Zhibin and Li, Hao}, 67 | title = {A simple baseline for semi-supervised semantic segmentation with strong data augmentation}, 68 | journal = {arXiv preprint arXiv:2104.07256}, 69 | year = {2021} 70 | } 71 | 72 | @article{CMB, 73 | author = {Alonso, Inigo and Sabater, Alberto and Ferstl, David and Montesano, Luis and Murillo, Ana C}, 74 | title = {Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank}, 75 | journal = {arXiv preprint arXiv:2104.13415}, 76 | year = {2021} 77 | } 78 | 79 | @inproceedings{DCC, 80 | author = {Lai, Xin and Tian, Zhuotao and Jiang, Li and Liu, Shu and Zhao, Hengshuang and Wang, Liwei and Jia, Jiaya}, 81 | title = {Semi-supervised semantic segmentation with directional context-aware consistency}, 82 | booktitle = {CVPR}, 83 | year = {2021} 84 | } 85 | 86 | @inproceedings{CPS, 87 | author = {Chen, Xiaokang and Yuan, Yuhui and Zeng, Gang and Wang, Jingdong}, 88 | title = {Semi-supervised semantic segmentation with cross pseudo supervision}, 89 | booktitle = {CVPR}, 90 | year = {2021} 91 | } -------------------------------------------------------------------------------- /bibfiles/human-detection.bib: -------------------------------------------------------------------------------- 1 | @inproceedings{HOG, 2 | author = {Dalal, Navneet and Triggs, Bill}, 3 | title = {Histograms of Oriented Gradients for Human Detection}, 4 | booktitle = {CVPR}, 5 | year = {2005} 6 | } 7 | 8 | @article{Caltech, 9 | author = {Dollar, Piotr and Wojek, Christian and Schiele, Bernt and Perona, Pietro}, 10 | title = {Pedestrian Detection: An Evaluation of the State of the Art}, 11 | volume = {34}, 12 | number = {4}, 13 | pages = {743-761}, 14 | journal = {T-PAMI}, 15 | year = {2012} 16 | } 17 | 18 | @inproceedings{KITTI, 19 | author = {Andreas Geiger and Philip Lenz and Raquel Urtasun}, 20 | title = {Are we ready for Autonomous Driving? The {KITTI} Vision Benchmark Suite}, 21 | booktitle = {CVPR}, 22 | year = {2012} 23 | } 24 | 25 | @inproceedings{UDN, 26 | author = {Ouyang, Wanli and Wang, Xiaogang}, 27 | title = {Joint Deep Learning for Pedestrian Detection}, 28 | booktitle = {ICCV}, 29 | year = {2014} 30 | } 31 | 32 | @inproceedings{DeepParts, 33 | author = {Tian, Yonglong and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou}, 34 | title = {Deep Learning Strong Parts for Pedestrian Detection}, 35 | booktitle = {ICCV}, 36 | year = {2015} 37 | } 38 | 39 | @inproceedings{RPN-BF, 40 | author = {Liliang Zhang and Liang Lin and Xiaodan Liang and Kaiming He}, 41 | title = {Is Faster {R-CNN} Doing Well for Pedestrian Detection?}, 42 | booktitle = {ECCV}, 43 | year = {2016} 44 | } 45 | 46 | @inproceedings{CityPersons, 47 | author = {Zhang, Shanshan and Benenson, Rodrigo and Schiele, Bernt}, 48 | title = {{CityPersons}: A Diverse Dataset for Pedestrian Detection}, 49 | booktitle = {CVPR}, 50 | year = {2017} 51 | } 52 | 53 | @article{CrowdHuman, 54 | author = {Shao, Shuai and Zhao, Zijian and Li, Boxun and Xiao, Tete and Yu, Gang and Zhang, Xiangyu and Sun, Jian}, 55 | title = {{CrowdHuman}: A Benchmark for Detecting Human in a Crowd}, 56 | journal = {arXiv preprint arXiv:1805.00123}, 57 | year = {2018} 58 | } 59 | 60 | @inproceedings{GuidedAttention, 61 | author = {Zhang, Shanshan and Yang, Jian and Schiele, Bernt}, 62 | title = {Occluded Pedestrian Detection Through Guided Attention in CNNs}, 63 | booktitle = {CVPR}, 64 | year = {2018} 65 | } 66 | 67 | @inproceedings{RepulsionLoss, 68 | author = {Wang, Xinlong and Xiao, Tete and Jiang, Yuning and Shao, Shuai and Sun, Jian and Shen, Chunhua}, 69 | title = {Repulsion Loss: Detecting Pedestrians in a Crowd}, 70 | booktitle = {CVPR}, 71 | year = {2018} 72 | } 73 | 74 | @inproceedings{OR-CNN, 75 | author = {Zhang, Shifeng and Wen, Longyin and Bian, Xiao and Lei, Zhen and Li, Stan Z.}, 76 | title = {Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd}, 77 | booktitle = {ECCV}, 78 | year = {2018} 79 | } 80 | 81 | @inproceedings{Bi-box, 82 | author = {Zhou, Chunluan and Yuan, Junsong}, 83 | title = {Bi-box Regression for Pedestrian Detection and Occlusion Estimation}, 84 | booktitle = {ECCV}, 85 | year = {2018} 86 | } 87 | 88 | @inproceedings{Graininess-Aware, 89 | author = {Lin, Chunze and Lu, Jiwen and Wang, Gang and Zhou, Jie}, 90 | title = {Graininess-Aware Deep Feature Learning for Pedestrian Detection}, 91 | booktitle = {ECCV}, 92 | year = {2018} 93 | } 94 | 95 | @inproceedings{CSP, 96 | author = {Liu, Wei and Liao, Shengcai and Ren, Weiqiang and Hu, Weidong and Yu, Yinan}, 97 | title = {High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection}, 98 | booktitle = {CVPR}, 99 | year = {2019} 100 | } 101 | 102 | @inproceedings{AdaptiveNMS, 103 | author = {Liu, Songtao and Huang, Di and Wang, Yunhong}, 104 | title = {Adaptive {NMS}: Refining Pedestrian Detection in a Crowd}, 105 | booktitle = {CVPR}, 106 | year = {2019} 107 | } 108 | 109 | @article{DoubleAnchor, 110 | author = {Kevin Zhang, Feng Xiong, Peize Sun, Li Hu, Boxun Li, Gang Yu}, 111 | title = {{Double Anchor R-CNN} for Human Detection in a Crowd}, 112 | journal = {arXiv preprint arXiv:1909.09998}, 113 | year = {2019} 114 | } -------------------------------------------------------------------------------- /bibfiles/2d-semantic-segmentation.bib: -------------------------------------------------------------------------------- 1 | @inproceedings{PASCAL-Context, 2 | author = {Roozbeh Mottaghi and Xianjie Chen and Xiaobai Liu and Nam-Gyu Cho and Seong-Whan Lee and Sanja Fidler and Raquel Urtasun and Alan Yuille}, 3 | title = {The Role of Context for Object Detection and Semantic Segmentation in the Wild}, 4 | booktitle = {CVPR}, 5 | year = {2014} 6 | } 7 | 8 | @article{PASCALVOC, 9 | author = {Everingham, Mark and Eslami, SM Ali and Van Gool, Luc and Williams, Christopher KI and Winn, John and Zisserman, Andrew}, 10 | title = {The pascal visual object classes challenge: A retrospective}, 11 | journal = {IJCV}, 12 | year = {2015} 13 | } 14 | 15 | @inproceedings{FCN, 16 | author = {Long, Jonathan and Shelhamer, Evan and Darrell, Trevor}, 17 | title = {Fully Convolutional Networks for Semantic Segmentation}, 18 | booktitle = {CVPR}, 19 | year = {2015} 20 | } 21 | 22 | @inproceedings{DeepLabv1, 23 | author = {Chen, Liang-Chieh and Papandreou, George and Kokkinos, Iasonas and Murphy, Kevin and Yuille, Alan L}, 24 | title = {Semantic image segmentation with deep convolutional nets and fully connected crfs}, 25 | booktitle = {ICLR}, 26 | year = {2015} 27 | } 28 | 29 | @inproceedings{UNet, 30 | author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas}, 31 | title = {U-net: Convolutional networks for biomedical image segmentation}, 32 | booktitle = {MICCAI}, 33 | year = {2015} 34 | } 35 | 36 | @inproceedings{DPN, 37 | author = {Liu, Ziwei and Li, Xiaoxiao and Luo, Ping and Loy, Chen-Change and Tang, Xiaoou}, 38 | title = {Semantic image segmentation via deep parsing network}, 39 | booktitle = {ICCV}, 40 | year = {2015} 41 | } 42 | 43 | @article{ENet, 44 | author = {Paszke, Adam and Chaurasia, Abhishek and Kim, Sangpil and Culurciello, Eugenio}, 45 | title = {Enet: A deep neural network architecture for real-time semantic segmentation}, 46 | journal = {arXiv preprint arXiv:1606.02147}, 47 | year = {2016} 48 | } 49 | 50 | @inproceedings{Cityscapes, 51 | author = {Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt}, 52 | title = {The Cityscapes Dataset for Semantic Urban Scene Understanding}, 53 | booktitle = {CVPR}, 54 | year = {2016} 55 | } 56 | 57 | @inproceedings{LRR, 58 | author = {Ghiasi, Golnaz and Fowlkes, Charless C}, 59 | title = {Laplacian pyramid reconstruction and refinement for semantic segmentation}, 60 | booktitle = {ECCV}, 61 | year = {2016} 62 | } 63 | 64 | @article{SegNet, 65 | author = {Badrinarayanan, Vijay and Kendall, Alex and Cipolla, Roberto}, 66 | title = {Segnet: A deep convolutional encoder-decoder architecture for image segmentation}, 67 | volume = {39}, 68 | number = {12}, 69 | pages = {2481-2495}, 70 | journal = {T-PAMI}, 71 | year = {2017} 72 | } 73 | 74 | @inproceedings{ADE20K, 75 | author = {Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio}, 76 | title = {Scene Parsing through ADE20K Dataset}, 77 | booktitle = {CVPR}, 78 | year = {2017} 79 | } 80 | 81 | @inproceedings{LC, 82 | author = {Li, Xiaoxiao and Liu, Ziwei and Luo, Ping and Change Loy, Chen and Tang, Xiaoou}, 83 | title = {Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade}, 84 | booktitle = {CVPR}, 85 | year = {2017} 86 | } 87 | 88 | @inproceedings{LargeKernelMatters, 89 | author = {Peng, Chao and Zhang, Xiangyu and Yu, Gang and Luo, Guiming and Sun, Jian}, 90 | title = {Large Kernel Matters--Improve Semantic Segmentation by Global Convolutional Network}, 91 | booktitle = {CVPR}, 92 | year = {2017} 93 | } 94 | 95 | @inproceedings{FRRN, 96 | author = {Pohlen, Tobias and Hermans, Alexander and Mathias, Markus and Leibe, Bastian}, 97 | title = {Full-resolution residual networks for semantic segmentation in street scenes}, 98 | booktitle = {CVPR}, 99 | year = {2017} 100 | } 101 | 102 | @article{DeepLabv3, 103 | author = {Chen, Liang-Chieh and Papandreou, George and Schroff, Florian and Adam, Hartwig}, 104 | title = {Rethinking atrous convolution for semantic image segmentation}, 105 | journal = {arXiv preprint arXiv:1706.05587}, 106 | year = {2017} 107 | } 108 | 109 | @inproceedings{PSPNet, 110 | author = {Zhao, Hengshuang and Shi, Jianping and Qi, Xiaojuan and Wang, Xiaogang and Jia, Jiaya}, 111 | title = {Pyramid scene parsing network}, 112 | booktitle = {CVPR}, 113 | year = {2017} 114 | } 115 | 116 | @inproceedings{RefineNet, 117 | author = {Lin, Guosheng and Milan, Anton and Shen, Chunhua and Reid, Ian}, 118 | title = {Refinenet: Multi-path refinement networks for high-resolution semantic segmentation}, 119 | booktitle = {CVPR}, 120 | year = {2017} 121 | } 122 | 123 | @article{DeepLabv2, 124 | author = {Chen, Liang-Chieh and Papandreou, George and Kokkinos, Iasonas and Murphy, Kevin and Yuille, Alan L}, 125 | title = {Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs}, 126 | volume = {40}, 127 | number = {4}, 128 | pages = {834-848}, 129 | journal = {T-PAMI}, 130 | year = {2018} 131 | } 132 | 133 | @article{OCNet, 134 | author = {Yuan, Yuhui and Wang, Jingdong}, 135 | title = {Ocnet: Object context network for scene parsing}, 136 | journal = {arXiv preprint arXiv:1809.00916}, 137 | year = {2018} 138 | } 139 | 140 | @inproceedings{HDC-DUC, 141 | author = {Wang, Panqu and Chen, Pengfei and Yuan, Ye and Liu, Ding and Huang, Zehua and Hou, Xiaodi and Cottrell, Garrison}, 142 | title = {Understanding convolution for semantic segmentation}, 143 | booktitle = {WACV}, 144 | year = {2018} 145 | } 146 | 147 | @inproceedings{DeepLabv3plus, 148 | author = {Chen, Liang-Chieh and Zhu, Yukun and Papandreou, George and Schroff, Florian and Adam, Hartwig}, 149 | title = {Encoder-decoder with atrous separable convolution for semantic image segmentation}, 150 | booktitle = {ECCV}, 151 | year = {2018} 152 | } 153 | 154 | @inproceedings{DenseASPP, 155 | author = {Yang, Maoke and Yu, Kun and Zhang, Chi and Li, Zhiwei and Yang, Kuiyuan}, 156 | title = {Denseaspp for semantic segmentation in street scenes}, 157 | booktitle = {CVPR}, 158 | year = {2018} 159 | } 160 | 161 | @inproceedings{DFN, 162 | author = {Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong}, 163 | title = {Learning a discriminative feature network for semantic segmentation}, 164 | booktitle = {CVPR}, 165 | year = {2018} 166 | } 167 | 168 | @inproceedings{EncNet, 169 | author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit}, 170 | title = {Context encoding for semantic segmentation}, 171 | booktitle = {CVPR}, 172 | year = {2018} 173 | } 174 | 175 | @inproceedings{ICNet, 176 | author = {Zhao, Hengshuang and Qi, Xiaojuan and Shen, Xiaoyong and Shi, Jianping and Jia, Jiaya}, 177 | title = {Icnet for real-time semantic segmentation on high-resolution images}, 178 | booktitle = {ECCV}, 179 | year = {2018} 180 | } 181 | 182 | @inproceedings{Exfuse, 183 | author = {Zhang, Zhenli and Zhang, Xiangyu and Peng, Chao and Xue, Xiangyang and Sun, Jian}, 184 | title = {Exfuse: Enhancing feature fusion for semantic segmentation}, 185 | booktitle = {ECCV}, 186 | year = {2018} 187 | } 188 | 189 | @inproceedings{BiSeNet, 190 | author = {Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong}, 191 | title = {Bisenet: Bilateral segmentation network for real-time semantic segmentation}, 192 | booktitle = {ECCV}, 193 | year = {2018} 194 | } 195 | 196 | @inproceedings{PSANet, 197 | author = {Zhao, Hengshuang and Zhang, Yi and Liu, Shu and Shi, Jianping and Change Loy, Chen and Lin, Dahua and Jia, Jiaya}, 198 | title = {Psanet: Point-wise spatial attention network for scene parsing}, 199 | booktitle = {ECCV}, 200 | year = {2018} 201 | } 202 | 203 | @inproceedings{MSCI, 204 | author = {Lin, Di and Ji, Yuanfeng and Lischinski, Dani and Cohen-Or, Daniel and Huang, Hui}, 205 | title = {Multi-scale context intertwining for semantic segmentation}, 206 | booktitle = {ECCV}, 207 | year = {2018} 208 | } 209 | 210 | @inproceedings{DANet, 211 | author = {Fu, Jun and Liu, Jing and Tian, Haijie and Li, Yong and Bao, Yongjun and Fang, Zhiwei and Lu, Hanqing}, 212 | title = {Dual attention network for scene segmentation}, 213 | booktitle = {CVPR}, 214 | year = {2019} 215 | } 216 | 217 | @inproceedings{CCNet, 218 | author = {Huang, Zilong and Wang, Xinggang and Huang, Lichao and Huang, Chang and Wei, Yunchao and Liu, Wenyu}, 219 | title = {CCNet: Criss-Cross Attention for Semantic Segmentation}, 220 | booktitle = {ICCV}, 221 | year = {2019} 222 | } 223 | 224 | @inproceedings{DUpsampling, 225 | author = {Tian, Zhi and He, Tong and Shen, Chunhua and Yan, Youliang}, 226 | title = {Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation}, 227 | booktitle = {CVPR}, 228 | year = {2019} 229 | } 230 | 231 | @inproceedings{Auto-DeepLab, 232 | author = {Liu, Chenxi and Chen, Liang-Chieh and Schroff, Florian and Adam, Hartwig and Hua, Wei and Yuille, Alan L and Fei-Fei, Li}, 233 | title = {Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation}, 234 | booktitle = {CVPR}, 235 | year = {2019} 236 | } 237 | -------------------------------------------------------------------------------- /bibfiles/image-level-weakly-supervised-segmentation.bib: -------------------------------------------------------------------------------- 1 | @inproceedings{MIL, 2 | author = {Pinheiro, Pedro O and Collobert, Ronan}, 3 | title = {From image-level to pixel-level labeling with convolutional networks}, 4 | booktitle = {CVPR}, 5 | year = {2015} 6 | } 7 | 8 | @inproceedings{EM-Adapt, 9 | author = {Papandreou, George and Chen, Liang-Chieh and Murphy, Kevin P and Yuille, Alan L}, 10 | title = {Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation}, 11 | booktitle = {ICCV}, 12 | year = {2015} 13 | } 14 | 15 | @inproceedings{CCNN, 16 | author = {Pathak, Deepak and Krahenbuhl, Philipp and Darrell, Trevor}, 17 | title = {Constrained convolutional neural networks for weakly supervised segmentation}, 18 | booktitle = {ICCV}, 19 | year = {2015} 20 | } 21 | 22 | @inproceedings{CAM, 23 | author = {Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and 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