├── README.md └── poster.gif /README.md: -------------------------------------------------------------------------------- 1 | ## 365 Days Computer Vision Learning LinkedIn Post 2 | 3 | Follow me on LinkedIn : https://www.linkedin.com/in/ashishpatel2604/ 4 | 5 | ![](https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post/blob/main/poster.gif) 6 | 7 | | Days | Topic | Post Link | 8 | | ---- | -------------------------------------------- | ---------------------- | 9 | | 1 | **EfficientDet** | https://bit.ly/362NWHa | 10 | | 2 | **Yolact++** | https://bit.ly/3o5OaU3 | 11 | | 3 | **YOLO Series** | https://bit.ly/3650LAJ | 12 | | 4 | **Detr** | https://bit.ly/39S5F57 | 13 | | 5 | **Vision Transformer** | https://bit.ly/39UMHLd | 14 | | 6 | **Dynamic RCNN** | https://bit.ly/3939gy5 | 15 | | 7 | **DeiT: (Data-efficient image Transformer)** | https://bit.ly/363ZABt | 16 | | 8 | **Yolov5** | https://bit.ly/39QHTXq | 17 | | 9 | **DropBlock** | https://bit.ly/3sM4TiG | 18 | | 10 | **FCN** | https://bit.ly/3iE9U8C | 19 | | 11 | **Unet** | https://bit.ly/3izdbG2 | 20 | | 12 | **RetinaNet** | https://bit.ly/3o5NrlN | 21 | | 13 | **SegNet** | https://bit.ly/3qIauVz | 22 | | 14 | **CAM** | https://bit.ly/2Y2I8ZR | 23 | | 15 | **R-FCN** | https://bit.ly/3iCKsQL | 24 | | 16 | **RepVGG** | https://bit.ly/2Y2pGjV | 25 | | 17 | **Graph Convolution Network** | https://bit.ly/2LS9RK8 | 26 | | 18 | **DeconvNet** | https://bit.ly/2Mhwzes | 27 | | 19 | **ENet** | https://bit.ly/2Y2HgEz | 28 | | 20 | **Deeplabv1** | https://bit.ly/3o7Utqn | 29 | | 21 | **CRF-RNN** | https://bit.ly/2Y5nsR4 | 30 | | 22 | **Deeplabv2** | https://bit.ly/2Y9DgSx | 31 | | 23 | **DPN** | https://bit.ly/363Cye2 | 32 | | 24 | **Grad-CAM** | https://bit.ly/3iF006q | 33 | | 25 | **ParseNet** | https://bit.ly/3oesFk5 | 34 | | 26 | **ResNeXt** | https://bit.ly/2M2sXxe | 35 | | 27 | **AmoebaNet** | https://bit.ly/2YgRIbN | 36 | | 28 | **DilatedNet** | https://bit.ly/2M9fuDS | 37 | | 29 | **DRN** | https://bit.ly/2KXVmUH | 38 | | 30 | **RefineNet** | https://bit.ly/3cpCBVq | 39 | | 31 | **Preactivation-Resnet** | https://bit.ly/2MJtgwQ | 40 | | 32 | **SqueezeNet** | https://bit.ly/3cv3Ca0 | 41 | | 33 | **FractalNet** | https://bit.ly/3pSv712 | 42 | | 34 | **PolyNet** | https://bit.ly/3atCQfJ | 43 | | 35 | **DeepSim(Image Quality Assessment)** | https://bit.ly/3oKJGTi | 44 | | 36 | **Residual Attention Network** | https://bit.ly/3cIjupL | 45 | | 37 | **IGCNet / IGCV** | https://bit.ly/36LRfTo | 46 | | 38 | **Resnet38** | https://bit.ly/2N7tpKL | 47 | | 39 | **SqueezeNext** | https://bit.ly/3cSev5W | 48 | | 40 | **Group Normalization** | https://bit.ly/3ryNxEI | 49 | | 41 | **ENAS** | https://bit.ly/2LB6pDC | 50 | | 42 | **PNASNet** | https://bit.ly/3tIX6mx | 51 | | 43 | **ShuffleNetV2** | https://bit.ly/2Zb3xAM | 52 | | 44 | **BAM** | https://bit.ly/3b67xb2 | 53 | | 45 | **CBAM** | https://bit.ly/3plxHvJ | 54 | | 46 | **MorphNet** | https://bit.ly/3rWzcSM | 55 | | 47 | **NetAdapt** | https://bit.ly/2NtlFmE | 56 | | 48 | **ESPNetv2** | https://bit.ly/3jWVoJv | 57 | | 49 | **FBNet** | https://bit.ly/3k1PXZL | 58 | | 50 | **HideandSeek** | https://bit.ly/3qELCP0 | 59 | | 51 | **MR-CNN & S-CNN** | https://bit.ly/2Zw6QTf | 60 | | 52 | **ACoL: Adversarial Complementary Learning** | https://bit.ly/3qKFNiU | 61 | | 53 | **CutMix** | https://bit.ly/2Nt5shI | 62 | | 54 | **ADL** | https://bit.ly/3qNeFQm | 63 | | 55 | **SAOL** | https://bit.ly/2NVuBBs | 64 | | 56 | **SSD** | https://bit.ly/37PWpyo | 65 | | 57 | **NOC** | https://bit.ly/3uBrZJJ | 66 | | 58 | **G-RMI** | https://bit.ly/3kJDlap | 67 | | 59 | **TDM** | https://bit.ly/3dV5zgN | 68 | | 60 | **DSSD** | https://bit.ly/3q6EHg8 | 69 | | 61 | **FPN** | https://bit.ly/2OewZn0 | 70 | | 62 | **DCN** | https://bit.ly/3e3G4Kg | 71 | | 63 | **Light-Head-RCNN** | https://bit.ly/388rtcT | 72 | | 64 | **Cascade RCNN** | https://bit.ly/3uUDlZz | 73 | | 65 | **MegNet** | https://bit.ly/3bkNvuM | 74 | | 66 | **StairNet** | https://bit.ly/3bluE2P | 75 | | 67 | **ImageNet Rethinking** | https://bit.ly/3bqBfZZ | 76 | | 68 | **ERFNet** | https://bit.ly/2OxgC5c | 77 | | 69 | **LayerCascade** | https://bit.ly/3qzWdd8 | 78 | | 70 | **IDW-CNN** | https://bit.ly/3letEAY | 79 | | 71 | **DIS** | https://bit.ly/3vi3xh3 | 80 | | 72 | **SDN** | https://bit.ly/3lftn0k | 81 | | 73 | **ResNet-DUC-HDC** | https://bit.ly/3lmdhlN | 82 | | 74 | **Deeplabv3+** | https://bit.ly/3lfSRuR | 83 | | 75 | **AutoDeeplab** | https://bit.ly/2P14kSF | 84 | | 76 | **c3** | https://bit.ly/3qX0yqK | 85 | | 77 | **DRRN** | https://bit.ly/3ltkWP9 | 86 | | 78 | **BR²Net** | https://bit.ly/3f0jGlI | 87 | | 79 | **SDS** | https://bit.ly/3f0CZLw | 88 | | 80 | **AdderNet** | https://bit.ly/3sfMdYa | 89 | | 81 | **HyperColumn** | https://bit.ly/3vV7Jn5 | 90 | | 82 | **DeepMask** | https://bit.ly/3cY2RVR | 91 | | 83 | **SharpMask** | https://bit.ly/3rg0h2r | 92 | | 84 | **MultipathNet** | https://bit.ly/31fcTMR | 93 | | 85 | **MNC** | https://bit.ly/39rRXqj | 94 | | 86 | **InstanceFCN** | https://bit.ly/3wbQuy8 | 95 | | 87 | **FCIS** | https://bit.ly/3dhPz6B | 96 | | 88 | **MaskLab** | https://bit.ly/3wb3Vya | 97 | | 89 | **PANet** | https://bit.ly/2PmQTNs | 98 | | 90 | **CUDMedVision1** | https://bit.ly/3rETZd1 | 99 | | 91 | **CUDMedVision2** | https://bit.ly/3mago0q | 100 | | 92 | **CFS-FCN** | https://bit.ly/3cXP0zX | 101 | | 93 | **U-net+Res-net** | https://bit.ly/3mpKD3P | 102 | | 94 | **Multi-Channel** | https://bit.ly/2Q1WCbN | 103 | | 95 | **V-Net** | https://bit.ly/3sYxGAt | 104 | | 96 | **3D-Unet** | https://bit.ly/3uvNOcS | 105 | | 97 | **M²FCN** | https://bit.ly/3cXSlPG | 106 | | 98 | **Suggestive Annotation** | https://bit.ly/3t1UbV8 | 107 | | 99 | **3D Unet + Resnet** | https://bit.ly/3wRu3i9 | 108 | | 100 | **Cascade 3D-Unet** | https://bit.ly/3siNsEX | 109 | | 101 | **DenseVoxNet** | https://bit.ly/2RGliYd | 110 | | 102 | **QSA + QNT** | https://bit.ly/3wWtyDf | 111 | | 103 | **Attention-Unet** | https://bit.ly/3eaMNAK | 112 | | 104 | **RUNet + R2Unet** | https://bit.ly/2Q4bIxG | 113 | | 105 | **VoxResNet** | https://bit.ly/32gLBWN | 114 | | 106 | **Unet++** | https://bit.ly/3esShGV | 115 | | 107 | **H-DenseUnet** | https://bit.ly/3dN53kn | 116 | | 108 | **DUnet** | https://bit.ly/3sPYrWS | 117 | | 109 | **MultiResUnet** | https://bit.ly/32J7Epr | 118 | | 110 | **Unet3+** | https://bit.ly/3vj4lRX | 119 | | 111 | **VGGNet For Covid19** | https://bit.ly/3ewquW6 | 120 | | 112 | 𝗗𝗲𝗻𝘀𝗲-𝗚𝗮𝘁𝗲𝗱 𝗨-𝗡𝗲𝘁 (𝗗𝗚𝗡𝗲𝘁) | https://bit.ly/3tR67cM | 121 | | 113 | **Ki-Unet** | https://bit.ly/3gD4wDK | 122 | | 114 | **Medical Transformer** | https://bit.ly/3dLw9Zf | 123 | | 115 | **Deep Snake- Instance Segmentation** | https://bit.ly/3dQmdhm | 124 | | 116 | **BlendMask** | https://bit.ly/32LVXyf | 125 | | 117 | **CenterNet** | https://bit.ly/3aJrJQD | 126 | | 118 | **SRCNN** | https://bit.ly/3t82eie | 127 | | 119 | **Swin Transformer** | https://bit.ly/2QMWxct | 128 | | 120 | **Polygon-RNN** | https://bit.ly/3ujEJ7D | 129 | | 121 | **PolyTransform** | https://bit.ly/3gT11ZZ | 130 | | 122 | **D2Det** | https://bit.ly/3b2EDJL | 131 | | 123 | **PolarMask** | https://bit.ly/3uklSsO | 132 | | 124 | **FGN** | https://bit.ly/3uiyyAl | 133 | | 125 | **Meta-SR** | https://bit.ly/3ekFyr9 | 134 | | 126 | **Iterative Kernel Correlation** | https://bit.ly/3xPGZp6 | 135 | | 127 | **SRFBN** | https://bit.ly/2Qc1c7z | 136 | | 128 | **ODE** | https://bit.ly/3w1K8k4 | 137 | | 129 | **SRNTT** | https://bit.ly/2RNT9hS | 138 | | 130 | **Parallax Attention** | https://bit.ly/3tIr74x | 139 | | 131 | **3D Super Resolution** | https://bit.ly/3bliXJa | 140 | | 132 | **FSTRN** | https://bit.ly/3uWJ8h7 | 141 | | 133 | **PointGroup** | https://bit.ly/2QfeKPP | 142 | | 134 | **3D-MPA** | https://bit.ly/3bqz9J6 | 143 | | 135 | **Saliency Propagation** | https://bit.ly/3tXTvj4 | 144 | | 136 | **Libra R-CNN** | https://bit.ly/3hDytnt | 145 | | 137 | **SiamRPN++** | https://bit.ly/33TNjyi | 146 | | 138 | **LoFTR** | https://bit.ly/3eUtlJS | 147 | | 139 | **MZSR** | https://bit.ly/3ul5gAs | 148 | | 140 | **UCTGAN** | https://bit.ly/3fQg9ox | 149 | | 141 | **OccuSeg** | https://bit.ly/3bUJtta | 150 | | 142 | **LAPGAN** | https://bit.ly/3unOjW1 | 151 | | 143 | **TPN** | https://bit.ly/3vvyIoW | 152 | | 144 | **GTAD** | https://bit.ly/3c09yqK | 153 | | 145 | **SlowFast** | https://bit.ly/3fMrI0d | 154 | | 146 | **IDU** | https://bit.ly/2ROcIa5 | 155 | | 147 | **ATSS** | https://bit.ly/3hTIflC | 156 | | 148 | **Attention-RPN** | https://bit.ly/3oYescY | 157 | | 149 | **Aug-FPN** | https://bit.ly/3fUbdzi | 158 | | 150 | **Hit-Detector** | https://bit.ly/3uGCLgB | 159 | | 151 | **MCN** | https://bit.ly/3ySpjtq | 160 | | 152 | **CentripetalNet** | https://bit.ly/2S1WNVB | 161 | | 153 | **ROAM** | https://bit.ly/34Ft8Ex | 162 | | 154 | **PF-NET(3D)** | https://bit.ly/2TzQiK9 | 163 | | 155 | **PointAugment** | https://bit.ly/3uMc8Hr | 164 | | 156 | **C-Flow** | https://bit.ly/3xgDlUn | 165 | | 157 | **RandLA-Net** | https://bit.ly/3fYajD9 | 166 | | 158 | **Total3DUnderStanding** | https://bit.ly/3v3jy9c | 167 | | 159 | **IF-Nets** | https://bit.ly/3v7XjPj | 168 | | 160 | **PerfectShape** | https://bit.ly/3za20vk | 169 | | 161 | **ACNe** | https://bit.ly/3gaJQSN | 170 | | 162 | **PQ-Net** | https://bit.ly/35dVPsm | 171 | | 163 | **SG-NN** | https://bit.ly/3iQ4yca | 172 | | 164 | **Cascade Cost Volume** | https://bit.ly/3gyZHtt | 173 | | 165 | **SketchGCN** | https://bit.ly/3pVoxI8 | 174 | | 166 | **Spektral (Graph Neural Network)** | https://bit.ly/3q2T079 | 175 | | 167 | **Graph Convolution Neural Network** | https://bit.ly/3gAkiNX | 176 | | 168 | **Fast Localized Spectral Filtering(Graph Kernel)** | https://bit.ly/3iRUEa0 | 177 | | 169 | **GraphSAGE** | https://bit.ly/3gCj9Xx | 178 | | 170 | **ARMA Convolution** | https://bit.ly/3qcubpC | 179 | | 171 | **Graph Attention Networks** | https://bit.ly/3h1gfKy | 180 | | 172 | **Axial-Deeplab** | https://bit.ly/3qiIF7l | 181 | | 173 | **Tide** | https://bit.ly/3j5evmh | 182 | | 174 | **SipMask** | https://bit.ly/3gMBoJE | 183 | | 175 | **UFO²** | https://bit.ly/2SVS2xA | 184 | | 176 | **SCAN** | https://bit.ly/2ThBv70 | 185 | | 177 | **AABO** : **Adaptive Anchor Box Optimization** | https://bit.ly/3qCSRaP | 186 | | 178 | **SimAug** | https://bit.ly/3dlV6tK | 187 | | 179 | **Instant-teaching** | https://bit.ly/3h0E2LU | 188 | | 180 | **Refinement Network for RGB-D** | https://bit.ly/3dtRh5O | 189 | | 181 | **Polka Lines** | https://bit.ly/3hlNbhd | 190 | | 182 | **HOTR** | https://bit.ly/3hsV44i | 191 | | 183 | **Soft-IntroVAE** | https://bit.ly/3jFozTk | 192 | | 184 | **ReXNet** | https://bit.ly/3r42WO9 | 193 | | 185 | **DiNTS** | https://bit.ly/3AQibii | 194 | | 186 | **Pose2Mesh** | https://bit.ly/3wFTORi | 195 | | 187 | **Keep Eyes on the Lane** | https://bit.ly/3wxs4hl | 196 | | 188 | **AssembleNet++** | https://bit.ly/3xAHhjf | 197 | | 189 | **SNE-RoadSeg** | https://bit.ly/3hyCEAL | 198 | | 190 | **AdvPC** | https://bit.ly/3i3dGrV | 199 | | 191 | **Eagle eye** | https://bit.ly/3e5Iqaz | 200 | | 192 | **Deep Hough Transform** | https://bit.ly/2UEFbAm | 201 | | 193 | **WeightNet** | https://bit.ly/3rfDSUL | 202 | | 194 | **StyleMAPGAN** | https://bit.ly/2URgPTO | 203 | | 195 | **PD-GAN** | https://bit.ly/3xQMCmM | 204 | | 196 | **Non-Local Sparse Attention** | https://bit.ly/3xJZbAd | 205 | | 197 | **TediGAN** | https://bit.ly/3wH67MZ | 206 | | 198 | **FedDG** | https://bit.ly/3zfKiGe | 207 | | 199 | **Auto-Exposure Fusion** | https://bit.ly/3y3F2W1 | 208 | | 200 | **Involution** | https://bit.ly/36Ksiaz | 209 | | 201 | **MutualNet** | https://bit.ly/3zhfd4N | 210 | | 202 | **Teachers do more than teach - Image to Image translation** | https://bit.ly/36RP28K | 211 | | 203 | **VideoMoCo** | https://bit.ly/3f6Pq7Z | 212 | | 204 | **ArtGAN** | https://bit.ly/3rvDCB9 | 213 | | 205 | **Vip-DeepLab** | https://bit.ly/3xmzmVX | 214 | | 206 | **PSConvolution** | https://bit.ly/3rEIgMY | 215 | | 207 | **Deep learning technique on Semantic Segmentation** | https://bit.ly/375hrID | 216 | | 208 | **Synthetic to Real** | https://bit.ly/3yfZSRO | 217 | | 209 | **Panoptic Segmentation** | https://bit.ly/376tbdA | 218 | | 210 | **HistoGAN** | https://bit.ly/3zSYyVD | 219 | | 211 | **Semantic Image Matting** | https://bit.ly/3s5ZD9F | 220 | | 212 | **Anchor-Free Person Search** | https://bit.ly/2VI0KAD | 221 | | 213 | **Spatial-Phase-Shallow-Learning** | https://bit.ly/3CDAl82 | 222 | | 214 | **LiteFlowNet3** | https://bit.ly/3yDILcO | 223 | | 215 | **EfficientNetv2** | https://bit.ly/3xAQsiE | 224 | | 216 | **CBNETv2** | https://bit.ly/3s3ptvb | 225 | | 217 | **PerPixel Classification** | https://bit.ly/3lOomyg | 226 | | 218 | **Kaleido-BERT** | https://bit.ly/3ywh2Lf | 227 | | 219 | **DARKGAN** | https://bit.ly/3lTW05J | 228 | | 220 | **PPDM** | https://bit.ly/3lPgjBt | 229 | | 221 | **SEAN** | https://bit.ly/3yOUJ3L | 230 | | 222 | **Closed-Loop Matters** | https://bit.ly/3CzBnlq | 231 | | 223 | **Elastic Graph Neural Network** | https://bit.ly/3jket9S | 232 | | 224 | **Deep Imbalance Regression** | https://bit.ly/3yn0Ue3 | 233 | | 225 | **PIPAL** - Image Quality Assessment | https://bit.ly/3gCliSx | 234 | | 226 | **Mobile-Former** | https://bit.ly/3kxCSbm | 235 | | 227 | **Rank and Sort Loss** | https://bit.ly/3sPQt1s | 236 | | 228 | **Room Classification using Graph Neural Network** | https://bit.ly/3gD8Odv | 237 | | 229 | **Pyramid Vision Transformer** | https://bit.ly/3zmod9h | 238 | | 230 | **EigenGAN** | https://bit.ly/3BfdIVO | 239 | | 231 | **GNeRF** | https://bit.ly/3mD3kTR | 240 | | 232 | **DetCo** | https://bit.ly/3sQiRk9 | 241 | | 233 | **DERT with Special Modulated Co-Attention** | https://bit.ly/3sPQ5jw | 242 | | | **Residual Attention** | https://bit.ly/3yni4bJ | 243 | | 235 | **MG-GAN** | https://bit.ly/3mD30o7 | 244 | | 236 | **Adaptable GAN Encoders** | https://bit.ly/3yh4XJ3 | 245 | | 237 | **AdaAttN** | https://bit.ly/3BepKPa | 246 | | 238 | **Conformer** | https://bit.ly/3gCkj4N | 247 | | 239 | **YOLOP** | https://bit.ly/3BicysB | 248 | | 240 | **VMNet** | https://bit.ly/3k73jFZ | 249 | | 241 | **Airbert** | https://bit.ly/3nvcrGs | 250 | | 242 | 𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗥-𝗖𝗡𝗡 | https://bit.ly/397Zius | 251 | | 243 | **Battle of Network Structure** | https://bit.ly/2XcHbB0 | 252 | | 244 | **InSeGAN** | https://bit.ly/3z9wyMF | 253 | | 245 | **Efficient Person Search** | https://bit.ly/3CpbZOr | 254 | | 246 | **DeepGCNs** | https://bit.ly/3AevSHg | 255 | | 247 | **GroupFormer** | https://bit.ly/3lqzm2Y | 256 | | 248 | **SLIDE** | https://bit.ly/3hwpiEp | 257 | | 249 | **Super Neuron** | https://bit.ly/3zkXE3D | 258 | | 250 | **SOTR** | https://bit.ly/3hvqCYl | 259 | | 251 | **Survey : Instance Segmentation** | https://bit.ly/3k90xQB | 260 | | 252 | **SO-Pose** | https://bit.ly/3C56KD8 | 261 | | 253 | **CANet** | https://bit.ly/2XlDKZ2 | 262 | | 254 | **XVFI** | https://bit.ly/3lrOpcZ | 263 | | 255 | **TxT** | https://bit.ly/3tGFlEH | 264 | | 256 | **ConvMLP** | https://bit.ly/2XlE8Xu | 265 | | 257 | **Cross Domain Contrastive Learning** | https://bit.ly/3tDb2id | 266 | | 258 | **OS2D: One Stage Object Detection** | https://bit.ly/3ufnEMD | 267 | | 259 | **PointManifoldCut** | https://bit.ly/3CKvAIL | 268 | | 260 | **Large Scale Facial Expression Dataset** | https://bit.ly/2ZqtT4V | 269 | | 261 | **Graph-FPN** | https://bit.ly/2XH8T9f | 270 | | 262 | **3D Shape Reconstruction** | https://bit.ly/2XTe9aq | 271 | | 263 | **Open Graph Benchmark Dataset** | https://bit.ly/3ET2Lfl | 272 | | 264 | **ShiftAddNet** | https://bit.ly/3i6eb5C | 273 | | 265 | **WatchOut! Motion Blurring the vision of your DNN** | https://bit.ly/3CKTzrw | 274 | | 266 | **Rethinking Learnable Tree Filter** | https://bit.ly/3zHfPAC | 275 | | 267 | **Neuron Merging** | https://bit.ly/39DwLNS | 276 | | 268 | **Distance IOU Loss** | https://bit.ly/3i7Zj6z | 277 | | 269 | **Deep Imitation learning** | https://bit.ly/3AzGVd6 | 278 | | 270 | **Pixel Level Cycle Association** | https://bit.ly/3iTZMK6 | 279 | | 271 | **Deep Model Fusion** | https://bit.ly/2YK45kl | 280 | | 272 | **Object Representation Network** | https://bit.ly/3BA0mnE | 281 | | 273 | **HOI Analysis** | https://bit.ly/3FH2Key | 282 | | 274 | **Deep Equilibrium Models** | https://bit.ly/3FDH2IB | 283 | | 275 | **Sampling from k-DPP** | https://bit.ly/3BAyRuc | 284 | | 276 | **Rotated Binary Neural Network** | https://bit.ly/3mIuYx3 | 285 | | 277 | **PP-LCNet** - **LightCNN** | https://bit.ly/3v1Zh5H | 286 | | 278 | **MC-Net+** | https://bit.ly/3v5tYqk | 287 | | 279 | **Fake it till you make it** | https://bit.ly/3AyGTSQ | 288 | | 280 | **Enformer** | https://bit.ly/3AAdCr9 | 289 | | 281 | **VideoClip** | https://bit.ly/3mOueGu | 290 | | 282 | **Moving Fashion** | https://bit.ly/3jdvAtN | 291 | | 283 | **Convolution to Transformer** | https://bit.ly/3v5yy8f | 292 | | 284 | **HeadGAN** | https://bit.ly/3BLzRvm | 293 | | 285 | **Focal Transformer** | https://bit.ly/3lvCYSI | 294 | | 286 | **StyleGAN3** | https://bit.ly/3kvFPKw | 295 | | 287 | **3Detr:3D Object Detection** | https://bit.ly/3Hfk6A8 | 296 | | 288 | **Do Self-Supervised and Supervised Methods Learn Similar Visual Representations?** | https://bit.ly/3kyWM6H | 297 | | 289 | **Back to the Features** | https://bit.ly/3kvsxh3 | 298 | | 290 | **Anticipative Video Transformer** | https://bit.ly/30mADl2 | 299 | | 291 | **Attention Meets Geometry** | https://bit.ly/3kweSpZ | 300 | | 292 | **DeepMoCaP:** Deep Optical Motion Capture | https://bit.ly/30mjTdT | 301 | | 293 | **TrOCR: Transformer-based Optical Character Recognition** | https://bit.ly/3DqenW5 | 302 | | 294 | **Moving Fashion** | https://bit.ly/2YGtjA1 | 303 | | 295 | **StyleNeRF** | https://bit.ly/31W4Mbz | 304 | | 296 | **ECA-Net: :Efficient Channel Attention** | https://bit.ly/3n92i1s | 305 | | 297 | **Inferring High Resolution Traffic Accident risk maps** | https://bit.ly/3HgovD6 | 306 | | 298 | **Bias Loss: For Mobile Neural Network** | https://bit.ly/3qvBPNO | 307 | | 299 | **ByteTrack: Multi-Object Tracking** | https://bit.ly/3c3l7wQ | 308 | | 300 | **Non-Deep Network** | https://bit.ly/3qwZwoV | 309 | | 301 | **Temporal Attentive Covariance** | https://bit.ly/3ontCbP | 310 | | 302 | **Plan-then-generate: Controlled Data to Text Generation** | https://bit.ly/3DcbsA6 | 311 | | 303 | **Dynamic Visual Reasoning** | https://bit.ly/31Q4BhP | 312 | | 304 | **MedMNIST: Medical MNIST Dataset** | https://bit.ly/3qxuqxq | 313 | | 305 | **Colossal-AI: A PyTorch-Based Deep Learning System For Large-Scale Parallel Training** | https://bit.ly/3wG6Xv8 | 314 | | 306 | **Recursively Embedded Atom Neural Network(REANN)** | https://bit.ly/3F1JKqe | 315 | | 307 | **PolyTrack: for fast multi-object tracking and segmentation** | https://bit.ly/3DeBmmS | 316 | | 308 | **Can contrastive learning avoid shortcut solutions?** | https://bit.ly/3wHJIk9 | 317 | | 309 | **ProjectedGAN: To Improve Image Quality** | https://bit.ly/30hw8Zm | 318 | | 310 | **Arch-Net: A Family Of Neural Networks Built With Operators To Bridge The Gap ** | https://bit.ly/3oFOCef | 319 | | 311 | **PP-ShiTu:A Practical Lightweight Image Recognition System** | https://bit.ly/3naurFw | 320 | | 312 | **EditGAN** | https://bit.ly/30gYd2Z | 321 | | 313 | **Panoptic 3D Scene Segmentation** | https://bit.ly/3caSvla | 322 | | 314 | **PARP: Improve the Efficiency of NN** | https://bit.ly/3DakTjt | 323 | | 315 | **WORD: Organ Segmentation Dataset** | https://bit.ly/3qv5OW2 | 324 | | 316 | **DenseULearn** | https://bit.ly/3ohRiyi | 325 | | 317 | **Does Thermal data make the detection systems more reliable?** | https://bit.ly/3sQgTSO | 326 | | 318 | **MADDNESS: Approximate Matrix Multiplication (AMM)** | https://bit.ly/3zgVIL4 | 327 | | 319 | **Deceive D: Adaptive Pseudo Augmentation** | https://bit.ly/3sIG6yA | 328 | | 320 | **OadTR** | https://bit.ly/3JsUHUF | 329 | | 321 | **OnePassImageNet** | https://bit.ly/3sKL6Ti | 330 | | 322 | **Image-specific Convolutional Kernel Modulation for Single Image Super-resolution** | https://bit.ly/3FUpA20 | 331 | | 323 | **TransMix** | https://bit.ly/3EH93gH | 332 | | 324 | **PytorchVideo** | https://bit.ly/3JvgDP7 | 333 | | 325 | **MetNet-2** | https://bit.ly/3sMZb2M | 334 | | 326 | **Unsupervised deep learning identifies semantic disentanglement** | https://bit.ly/3JyAwVi | 335 | | 327 | **Story Visualization** | https://bit.ly/3qB554i | 336 | | 328 | **MetaFormer** | https://bit.ly/3sLBebP | 337 | | 329 | **GauGAN2** | https://bit.ly/3pGrIVH | 338 | | 330 | **SciGAP** | https://bit.ly/3EB7e4U | 339 | | 331 | **Generative Flow Networks (GFlowNets)** | https://bit.ly/3Jv9YEz | 340 | | 332 | **Ensemble Inversion** | https://bit.ly/3ECwbg9 | 341 | | 333 | **SAVi** | https://bit.ly/3eF6txe | 342 | | 334 | **Digital Optical Neural Network** | https://bit.ly/3EI07rh | 343 | | 335 | **Image-Generation Research With Manifold Matching Via Metric Learning** | https://bit.ly/3FUomnq | 344 | | 336 | **GHN-2(Graph HyperNetworks)** | https://bit.ly/3qzc5yB | 345 | | 337 | **NeatNet** | https://bit.ly/3sLY17r | 346 | | 338 | **NeuralProphet** | https://bit.ly/3JrUK38 | 347 | | 339 | **Background Activation Suppression for Weakly Supervised Object Detection** | https://bit.ly/3Jvyzt2 | 348 | | 340 | **Learning to Detect Every Thing in an Open World** | https://bit.ly/3mKxOTc | 349 | | 341 | **PoolFormer** | https://bit.ly/3qFHNtS | 350 | | 342 | **GLIP** | https://bit.ly/3mK3bgx | 351 | | 343 | **PHALP** | https://bit.ly/3eJJvEV | 352 | | 344 | **PixMix** | https://bit.ly/3Hqh77m | 353 | | 345 | **CodeNet** | https://bit.ly/32RPx3X | 354 | | 346 | **GANgealing** | https://bit.ly/3EIkO6k | 355 | | 347 | **Semantic Diffusion Guidance** | https://bit.ly/3JsNzI3 | 356 | | 348 | **TokenLearner** | https://bit.ly/3mLG4lM | 357 | | 349 | **Temporal Fusion Transformer (TFT)** | https://bit.ly/3JuHcno | 358 | | 350 | **HiClass: Evaluation Metrics for Local Hierarchical Classification** | https://bit.ly/3JHmn8H | 359 | | 351 | **Stable Long Term Recurrent Video Super Resolution** | https://bit.ly/3qFlPHl | 360 | | 352 | **AdaViT** | https://bit.ly/3eDASMj | 361 | | 353 | **Few-Shot Learner (FSL)** | https://bit.ly/3ELOOym | 362 | | 354 | **Exemplar Transformers** | https://bit.ly/3qzJE3C | 363 | | 355 | **StyleSwin** | https://bit.ly/3HqkCe4 | 364 | | 356 | **RepMLNet** | https://bit.ly/32DxbUu | 365 | | 357 | **2 Stage Unet** | https://bit.ly/3JGjIMq | 366 | | 358 | **Untrained Deep NN** | https://bit.ly/3JplL7r | 367 | | 359 | **SeMask** | https://bit.ly/3zfouM8 | 368 | | 360 | **JoJoGAN** | https://bit.ly/31gl9Qi | 369 | | 361 | **ELSA** | https://bit.ly/3mLWScb | 370 | | 362 | **PRIME** | https://bit.ly/3FI14RZ | 371 | | 363 | **GLIDE** | https://bit.ly/31ixB20 | 372 | | 364 | **StyleGAN-V** | https://bit.ly/3Jvx91G | 373 | | 365 | **SLIP: Self-supervision meets Language-Image Pre-training** | https://bit.ly/3qAjL3r | 374 | | 366 | **SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos** | https://bit.ly/3tYNxlp | 375 | | 367 | **Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results** | https://bit.ly/3tZFyEQ | 376 | | 368 | **PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability** | https://bit.ly/3LCKENk | 377 | | 369 | **Vision Transformer with Deformable Attention** | https://bit.ly/3tY3s3k | 378 | | 370 | **A Transformer-Based Siamese Network for Change Detection** | https://bit.ly/3DxPYP5 | 379 | | 371 | **Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention** | https://bit.ly/3qRsTle | 380 | | 372 | **SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection** | https://bit.ly/3tXduls | 381 | | 373 | **HyperionSolarNet: Solar Panel Detection from Aerial Images** | https://bit.ly/35v2rX6 | 382 | | 374 | **Realistic Full-Body Anonymization with Surface-Guided GANs** | https://bit.ly/3DwBNd4 | 383 | | 375 | **Generalized Category Discovery** | https://bit.ly/3IZ1HaC | 384 | | 376 | **KerGNNs: Interpretable Graph Neural Networks with Graph Kernels** | https://bit.ly/3DtWtlU | 385 | | 377 | **Optimization Planning for 3D ConvNets** | https://bit.ly/3K38e5p | 386 | | 378 | **gDNA: Towards Generative Detailed Neural Avatars** | https://bit.ly/3DEtFHC | 387 | | 379 | **SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps** | https://bit.ly/3NIieTA | 388 | | 380 | **HYDLA: Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning** | https://bit.ly/379dy8v | 389 | | 381 | **HardBoost: Boosting Zero-Shot Learning with Hard Classes** | https://bit.ly/379diX5 | 390 | | 382 | **DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images** | https://bit.ly/3Lu0UzU | 391 | | 383 | **Q-ViT: Fully Differentiable Quantization for Vision Transformer** | https://bit.ly/3qXv9Ym | 392 | | 384 | **SPAMs: Structured Implicit Parametric Models** | https://bit.ly/3iU95cL | 393 | | 385 | **GeoFill: Reference-Based Image Inpainting of Scenes with Complex Geometry** | https://bit.ly/3qUwCP6 | 394 | | 386 | **Improving language models by retrieving from trillions of tokens** | https://bit.ly/37aKsG5 | 395 | | 387 | **StylEx finds and visualizes disentangled attributes that affect a classifier automatically.** | https://bit.ly/3qYwYEf | 396 | | 388 | **‘ReLICv2’: Pushing The Limits of Self-Supervised ResNet** | https://bit.ly/3JZXy7C | 397 | | 389 | **‘Detic’: A Method to Detect Twenty-Thousand Classes using Image-Level Supervision** | https://bit.ly/3iRtsqZ | 398 | | 390 | **Momentum Capsule Networks** | https://bit.ly/3NFDv0j | 399 | | 391 | **RelTR: Relation Transformer for Scene Graph Generation** | https://bit.ly/3iVBWgB | 400 | | 392 | **Transformer based SAR Images Despecking** | https://bit.ly/3qWeILH | 401 | | 393 | **ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation** | https://bit.ly/3wWGY4T | 402 | | 394 | **VRT: A Video Restoration Transformer** | https://bit.ly/3K44YXw | 403 | | 395 | **You Only Cut Once: Boosting Data Augmentation with a Single Cut** | https://bit.ly/36L8pDW | 404 | | 396 | **StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets** | https://bit.ly/3iRlEp8 | 405 | | 397 | **The KFIoU Loss for Rotated Object Detection** | https://bit.ly/3NHUL5e | 406 | | 398 | **The Met Dataset: Instance Level Recognition** | https://bit.ly/3K7lPJ2 | 407 | | 399 | **Alphacode: a System that can compete at average human level** | https://bit.ly/3qXIIH5 | 408 | | 400 | **Third Time's the Charm? Image and Video Editing with StyleGAN3** | https://bit.ly/35vAoqx | 409 | | 401 | **NeuralFusion: Online Depth Fusion in Latent Space** | https://bit.ly/3uFaysA | 410 | | 402 | **VOS: Learning what you don't know by VIRTUAL OUTLIER SYNTHESIS** | https://bit.ly/3uPG9rG | 411 | | 403 | **Self-Conditioned Generative Adversarial Networks for Image Editing** | https://bit.ly/3tX8m0u | 412 | | 404 | **TransformNet: Self-supervised representation learning through predicting geometric transformations** | https://bit.ly/3uOCfPM | 413 | | 405 | **YOLOv7 - Framework Beyond Detection** | https://bit.ly/3wXU81y | 414 | | 406 | **F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization** | https://bit.ly/3DzhFXU | 415 | | 407 | **Block-NeRF: Scalable Large Scene Neural View Synthesis** | https://bit.ly/3LyELk5 | 416 | | 408 | **Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition** | https://bit.ly/375C76y | 417 | | 409 | **COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets** | https://bit.ly/3NCK6bZ | 418 | | 410 | **ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification** | https://bit.ly/3uJuMBz | 419 | | 411 | **Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges** | https://bit.ly/388imeT | 420 | | 412 | **How Do Vision Transformers Work?** | https://bit.ly/3NE1mO2 | 421 | | 413 | **Mirror-Yolo: An attention-based instance segmentation and detection model for mirrors** | https://bit.ly/3LBS96P | 422 | | 414 | **PENCIL: Deep Learning with Noisy Labels** | https://bit.ly/3iXvHc4 | 423 | | 415 | **VLP: A Survey on Vision-Language Pre-training** | https://bit.ly/3J0v2RZ | 424 | | 416 | **Visual Attention Network** | https://bit.ly/3Dt7rbv | 425 | | 417 | **GroupViT: Semantic Segmentation Emerges from Text Supervision** | https://bit.ly/3NQv7eG | 426 | | 418 | **Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis** | https://bit.ly/373xs4T | 427 | | 419 | **End to End Cascaded Image De-raining and Object Detetion NN** | https://bit.ly/375PLGw | 428 | | 420 | **Level-K to Nash Equilibrium** | https://bit.ly/3NFRX8t | 429 | | 421 | **Machine Learning for Mechanical Ventilation Control** | https://bit.ly/3JZCMEV | 430 | | 422 | **The effect of fatigue on the performance of online writer recognition** | https://bit.ly/3wXSSLS | 431 | | 423 | **State-of-the-Art in the Architecture, Methods and Applications of StyleGAN** | https://bit.ly/3iRjl5s | 432 | | 424 | **Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation** | https://bit.ly/3v5XZXR | 433 | | 425 | **Self-supervised Transformer for Deepfake Detection** | https://bit.ly/3tXtUdk | 434 | | 426 | **CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size** | https://bit.ly/3LxkrQa | 435 | | 427 | **TCTrack: Temporal Contexts for Aerial Tracking** | https://bit.ly/3uM5O4B | 436 | | 428 | **LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction** | https://bit.ly/3uOfKe0 | 437 | | 429 | **HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening** | https://bit.ly/35tRV2j | 438 | | 430 | **ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization** | https://bit.ly/3LwoMmy | 439 | | 431 | **MLSeg: Image and Video Segmentation** | https://bit.ly/38p9iCN | 440 | | 432 | **Image Steganography based on Style Transfer** | https://bit.ly/3DJHLaN | 441 | | 433 | **GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains** | https://bit.ly/3JYPrIg | 442 | | 434 | **AGCN: Augmented Graph Convolutional Network** | https://bit.ly/3DwZrWN | 443 | | 435 | **StyleBabel: Artistic Style Tagging and Captioning** | https://bit.ly/3j1Klit | 444 | | 436 | **ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI** | https://bit.ly/38maN4z | 445 | | 437 | **InsetGAN for Full-Body Image Generation** | https://bit.ly/3Dsu9At | 446 | | 438 | **Implicit Feature Decoupling with Depthwise Quantization** | https://bit.ly/3K1mxaA | 447 | | 439 | **Bamboo: Building Mega-Scale Vision Dataset** | https://bit.ly/3wVPalD | 448 | | 440 | **TensoRF: Tensorial Radiance Fields** | https://bit.ly/3iWAFWI | 449 | | 441 | **FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition** | https://bit.ly/3NCHTxd | 450 | | 442 | **One-Shot Adaptation of GAN in Just One CLIP** | https://bit.ly/36NOPab | 451 | | 443 | **SHREC 2021: Classification in cryo-electron tomograms** | https://bit.ly/3iSXpqv | 452 | | 444 | **MaskGIT: Masked Generative Image Transformer** | https://bit.ly/3qSQz8I | 453 | | 445 | **Detection, Recognition, and Tracking: A Survey** | https://bit.ly/378G8qw | 454 | | 446 | **Mixed Differential Privacy** | https://bit.ly/3IZ0MGU | 455 | | 447 | **Mixed DualStyleGAN** | https://bit.ly/3wTyAmD | 456 | | 448 | **BigDetection** | https://bit.ly/3DuZSRk | 457 | | 449 | **Feature visualization for convolutional neural network** | https://bit.ly/3Dwf6FJ | 458 | | 450 | **AutoAvatar** | https://bit.ly/38m9ClF | 459 | | 451 | **A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction** | https://bit.ly/3Dz1idF | 460 | | 452 | **StyleT2I** | https://bit.ly/35u5Wx0 | 461 | | 453 | **L^3U-net** | https://bit.ly/3iTOq8r | 462 | | 454 | **Balanced MSE** | https://bit.ly/3rxt7yo | 463 | | 455 | **BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers** | https://bit.ly/36m3HfC | 464 | | 456 | **TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing** | https://bit.ly/3JQKZKS | 465 | | 457 | **On the Importance of Asymmetry for Siamese Representation Learning** | https://bit.ly/3JNgcyt | 466 | | 458 | **On One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks** | https://bit.ly/3uQTC3P | 467 | | 459 | **Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth** | https://bit.ly/3KWT6a4 | 468 | | 460 | **Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection** | https://bit.ly/3L8a59H | 469 | | 461 | **DaViT: Dual Attention Vision Transformers** | https://bit.ly/3Engc7e | 470 | | 462 | **SPAct: Self-supervised Privacy Preservation for Action Recognition** | https://bit.ly/3KTNvRW | 471 | | 463 | **Class-Incremental Learning with Strong Pre-trained Models** | https://bit.ly/3MdlcOq | 472 | | 464 | **RBGNet: Ray-based Grouping for 3D Object Detection by Center for Data Science** | https://bit.ly/3EqkydH | 473 | | 465 | **Event Transformer** | https://bit.ly/3KUsMxc | 474 | | 466 | **ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension** | https://bit.ly/3M6RgDE | 475 | | 467 | **A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research** | https://bit.ly/3xAyqRj | 476 | | 468 | **Simple Baselines for Image Restoration** | https://bit.ly/3vt4tjB | 477 | | 469 | **Masked Siamese Networks for Label-Efficient Learning** | https://bit.ly/3viEs6s | 478 | | 470 | **Neighborhood Attention Transformer** | https://bit.ly/3jNExK3 | 479 | | 471 | **TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation** | https://bit.ly/3M3EA0K | 480 | | 472 | **MVSTER: Epipolar Transformer for Efficient Multi-View Stereo** | https://bit.ly/3MaDTCR | 481 | | 473 | **Temporally Efficient Vision Transformer for Video Instance Segmentation** | https://bit.ly/3w6xkf3 | 482 | | 474 | **EditGAN: High-Precision Semantic Image Editing** | https://bit.ly/3yx2JJ2 | 483 | | 475 | **CenterNet++ for Object Detection** | https://bit.ly/3woxrBG | 484 | | 476 | **A case for using rotation invariant features in state of the art feature matchers** | https://bit.ly/3kZ1x9A | 485 | | 477 | **WebFace260M: A Benchmark for Million-Scale Deep Face Recognition** | https://bit.ly/3w2T3Vd | 486 | | 478 | **JIFF: Jointly-aligned Implicit Face Function for High-Quality Single View Clothed Human Reconstruction** | https://bit.ly/3N9Me9U | 487 | | 479 | **Image Data Augmentation for Deep Learning: A Survey** | https://bit.ly/3PfC1uA | 488 | | 480 | **StyleGAN-Human: A Data-Centric Odyssey of Human Generation** | https://bit.ly/3PqV710 | 489 | | 481 | **Few-shot Head Swapping In The Wild Secrets Revealed By Department Of Computer Vision Technology (vis)** | https://bit.ly/3w7xm6c | 490 | | 482 | **CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP** | https://bit.ly/3N3cEKu | 491 | | 483 | **HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling** | https://bit.ly/3Nqnevx | 492 | | 484 | **Generative Adversarial Networks for Image Super-Resolution: A Survey** | https://bit.ly/39jyL0U | 493 | | 485 | **CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification** | https://bit.ly/3N7Qd6V | 494 | | 486 | **C3-STISR: Scene Text Image Super-resolution with Triple Clues** | https://bit.ly/3l1352C | 495 | | 487 | **Barbershop: GAN-based Image Compositing using Segmentation Masks** | https://bit.ly/39hus6d | 496 | | 488 | **DANBO: Disentangled Articulated Neural Body Representations** | https://bit.ly/3LkqWp3 | 497 | | 489 | **BlobGAN: Spatially Disentangled Scene Representations** | https://bit.ly/3sufEYz | 498 | | 490 | **Text to artistic image generation** | https://bit.ly/3w6wzmd | 499 | | 491 | **Sequencer: Deep LSTM for Image Classification** | https://bit.ly/3sulPvT | 500 | | 492 | **IVY: An Open-Source Tool To Make Deep Learning Code Compatible Across Frameworks** | https://bit.ly/3M6MbvJ | 501 | | 493 | **Introspective Deep Metric Learning** | https://bit.ly/3w2pZ02 | 502 | | 494 | **KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints** | https://bit.ly/3wnRhwF | 503 | | 495 | **GraphWorld: A Methodology For Analyzing The Performance Of GNN Architectures On Millions Of Synthetic Benchmark Datasets** | https://bit.ly/3PUQexk | 504 | | 496 | **Group R-CNN for Weakly Semi-supervised Object Detection with Points** | https://bit.ly/3zfvU3W | 505 | | 497 | **Few-Shot Head Swapping in the Wild** | https://bit.ly/3xapGkn | 506 | | 498 | **StyLandGAN: A StyleGAN based Landscape Image Synthesis using Depth-map** | https://bit.ly/3GKX4Bi | 507 | | 499 | **Spiking Approximations of the MaxPooling Operation in Deep SNNs** | https://bit.ly/3GLp7AG | 508 | | **500** | **Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization** | https://bit.ly/3NTGsJQ | 509 | 510 | ***Thanks for Reading🎉🎉🎉🎉*** 511 | 512 | ---- 513 | 514 | -------------------------------------------------------------------------------- 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