{"id":13935225,"url":"https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post","last_synced_at":"2025-07-19T20:31:21.276Z","repository":{"id":37263592,"uuid":"331881611","full_name":"ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post","owner":"ashishpatel26","description":"365 Days Computer Vision Learning Linkedin 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+ 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲"],"sub_categories":[],"readme":"## 365 Days Computer Vision Learning LinkedIn Post\r\n\r\nFollow me on LinkedIn : https://www.linkedin.com/in/ashishpatel2604/\r\n\r\n![](https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post/blob/main/poster.gif)\r\n\r\n| Days | Topic                                        | Post Link              |\r\n| ---- | -------------------------------------------- | ---------------------- |\r\n| 1    | **EfficientDet**                             | https://bit.ly/362NWHa |\r\n| 2    | **Yolact++**                                 | https://bit.ly/3o5OaU3 |\r\n| 3    | **YOLO Series**                              | https://bit.ly/3650LAJ |\r\n| 4    | **Detr**                                     | https://bit.ly/39S5F57 |\r\n| 5    | **Vision Transformer**                       | https://bit.ly/39UMHLd |\r\n| 6    | **Dynamic RCNN**                             | https://bit.ly/3939gy5 |\r\n| 7    | **DeiT: (Data-efficient image Transformer)** | https://bit.ly/363ZABt |\r\n| 8    | **Yolov5**                                   | https://bit.ly/39QHTXq |\r\n| 9    | **DropBlock**                                | https://bit.ly/3sM4TiG |\r\n| 10   | **FCN**                                      | https://bit.ly/3iE9U8C |\r\n| 11   | **Unet**                                     | https://bit.ly/3izdbG2 |\r\n| 12   | **RetinaNet**                                | https://bit.ly/3o5NrlN |\r\n| 13   | **SegNet**                                   | https://bit.ly/3qIauVz |\r\n| 14   | **CAM**                                      | https://bit.ly/2Y2I8ZR |\r\n| 15   | **R-FCN**                                    | https://bit.ly/3iCKsQL |\r\n| 16   | **RepVGG**                                   | https://bit.ly/2Y2pGjV |\r\n| 17   | **Graph Convolution Network**                | https://bit.ly/2LS9RK8 |\r\n| 18   | **DeconvNet**                                | https://bit.ly/2Mhwzes |\r\n| 19   | **ENet**                                     | https://bit.ly/2Y2HgEz |\r\n| 20   | **Deeplabv1**                                | https://bit.ly/3o7Utqn |\r\n| 21   | **CRF-RNN**                                  | https://bit.ly/2Y5nsR4 |\r\n| 22   | **Deeplabv2**                                | https://bit.ly/2Y9DgSx |\r\n| 23   | **DPN**                                      | https://bit.ly/363Cye2 |\r\n| 24   | **Grad-CAM**                                 | https://bit.ly/3iF006q |\r\n| 25   | **ParseNet**                                 | https://bit.ly/3oesFk5 |\r\n| 26   | **ResNeXt**                                  | https://bit.ly/2M2sXxe |\r\n| 27   | **AmoebaNet**                                | https://bit.ly/2YgRIbN |\r\n| 28   | **DilatedNet**                               | https://bit.ly/2M9fuDS |\r\n| 29   | **DRN**                                      | https://bit.ly/2KXVmUH |\r\n| 30   | **RefineNet**                                | https://bit.ly/3cpCBVq |\r\n| 31   | **Preactivation-Resnet**                     | https://bit.ly/2MJtgwQ |\r\n| 32   | **SqueezeNet**                               | https://bit.ly/3cv3Ca0 |\r\n| 33   | **FractalNet**                               | https://bit.ly/3pSv712 |\r\n| 34   | **PolyNet**                                  | https://bit.ly/3atCQfJ |\r\n| 35   | **DeepSim(Image Quality Assessment)**        | https://bit.ly/3oKJGTi |\r\n| 36   | **Residual Attention Network**               | https://bit.ly/3cIjupL |\r\n| 37   | **IGCNet / IGCV**                            | https://bit.ly/36LRfTo |\r\n| 38   | **Resnet38**                                 | https://bit.ly/2N7tpKL |\r\n| 39   | **SqueezeNext**                              | https://bit.ly/3cSev5W |\r\n| 40   | **Group Normalization**                      | https://bit.ly/3ryNxEI |\r\n| 41   | **ENAS**                                     | https://bit.ly/2LB6pDC |\r\n| 42   | **PNASNet**                                  | https://bit.ly/3tIX6mx |\r\n| 43   | **ShuffleNetV2**                             | https://bit.ly/2Zb3xAM |\r\n| 44   | **BAM**                                      | https://bit.ly/3b67xb2 |\r\n| 45   | **CBAM**                                     | https://bit.ly/3plxHvJ |\r\n| 46   | **MorphNet**                                 | https://bit.ly/3rWzcSM |\r\n| 47   | **NetAdapt**                                 | https://bit.ly/2NtlFmE |\r\n| 48   | **ESPNetv2**                                 | https://bit.ly/3jWVoJv |\r\n| 49   | **FBNet**                                    | https://bit.ly/3k1PXZL |\r\n| 50   | **HideandSeek**                              | https://bit.ly/3qELCP0 |\r\n| 51   | **MR-CNN \u0026 S-CNN**                           | https://bit.ly/2Zw6QTf |\r\n| 52   | **ACoL: Adversarial Complementary Learning** | https://bit.ly/3qKFNiU |\r\n| 53   | **CutMix**                                   | https://bit.ly/2Nt5shI |\r\n| 54   | **ADL**                                      | https://bit.ly/3qNeFQm |\r\n| 55   | **SAOL**                                     | https://bit.ly/2NVuBBs |\r\n| 56   | **SSD**                                      | https://bit.ly/37PWpyo |\r\n| 57   | **NOC**                                      | https://bit.ly/3uBrZJJ |\r\n| 58   | **G-RMI**                                    | https://bit.ly/3kJDlap |\r\n| 59   | **TDM**                                      | https://bit.ly/3dV5zgN |\r\n| 60   | **DSSD**                                     | https://bit.ly/3q6EHg8 |\r\n| 61   | **FPN**                                      | https://bit.ly/2OewZn0 |\r\n| 62   | **DCN**                                      | https://bit.ly/3e3G4Kg |\r\n| 63   | **Light-Head-RCNN**                          | https://bit.ly/388rtcT |\r\n| 64   | **Cascade RCNN**                             | https://bit.ly/3uUDlZz |\r\n| 65   | **MegNet**                                   | https://bit.ly/3bkNvuM |\r\n| 66   | **StairNet**                                 | https://bit.ly/3bluE2P |\r\n| 67   | **ImageNet Rethinking**                      | https://bit.ly/3bqBfZZ |\r\n| 68   | **ERFNet**                                   | https://bit.ly/2OxgC5c |\r\n| 69   | **LayerCascade**                             | https://bit.ly/3qzWdd8 |\r\n| 70   | **IDW-CNN**                                  | https://bit.ly/3letEAY |\r\n| 71   | **DIS**                                      | https://bit.ly/3vi3xh3 |\r\n| 72   | **SDN**                                      | https://bit.ly/3lftn0k |\r\n| 73   | **ResNet-DUC-HDC**                           | https://bit.ly/3lmdhlN |\r\n| 74   | **Deeplabv3+**                               | https://bit.ly/3lfSRuR |\r\n| 75   | **AutoDeeplab**                              | https://bit.ly/2P14kSF |\r\n| 76   | **c3**                                       | https://bit.ly/3qX0yqK |\r\n| 77   | **DRRN**                                     | https://bit.ly/3ltkWP9 |\r\n| 78   | **BR²Net**                                   | https://bit.ly/3f0jGlI |\r\n| 79   | **SDS**                                      | https://bit.ly/3f0CZLw |\r\n| 80   | **AdderNet**                                 | https://bit.ly/3sfMdYa |\r\n| 81   | **HyperColumn**                              | https://bit.ly/3vV7Jn5 |\r\n| 82   | **DeepMask**                                 | https://bit.ly/3cY2RVR |\r\n| 83   | **SharpMask**                                | https://bit.ly/3rg0h2r |\r\n| 84   | **MultipathNet**                             | https://bit.ly/31fcTMR |\r\n| 85   | **MNC**                                      | https://bit.ly/39rRXqj |\r\n| 86   | **InstanceFCN**                              | https://bit.ly/3wbQuy8 |\r\n| 87   | **FCIS**                                     | https://bit.ly/3dhPz6B |\r\n| 88   | **MaskLab**                                  | https://bit.ly/3wb3Vya |\r\n| 89   | **PANet**                                    | https://bit.ly/2PmQTNs |\r\n| 90   | **CUDMedVision1**                            | https://bit.ly/3rETZd1 |\r\n| 91   | **CUDMedVision2**                            | https://bit.ly/3mago0q |\r\n| 92   | **CFS-FCN**                                  | https://bit.ly/3cXP0zX |\r\n| 93   | **U-net+Res-net**                            | https://bit.ly/3mpKD3P |\r\n| 94   | **Multi-Channel**                            | https://bit.ly/2Q1WCbN |\r\n| 95   | **V-Net**                                    | https://bit.ly/3sYxGAt |\r\n| 96   | **3D-Unet**                                  | https://bit.ly/3uvNOcS |\r\n| 97   | **M²FCN**                                    | https://bit.ly/3cXSlPG |\r\n| 98   | **Suggestive Annotation**                    | https://bit.ly/3t1UbV8 |\r\n| 99   | **3D Unet + Resnet**                         | https://bit.ly/3wRu3i9 |\r\n| 100  | **Cascade 3D-Unet**                          | https://bit.ly/3siNsEX |\r\n| 101  | **DenseVoxNet**                              | https://bit.ly/2RGliYd |\r\n| 102  | **QSA + QNT**                                | https://bit.ly/3wWtyDf |\r\n| 103  | **Attention-Unet**                           | https://bit.ly/3eaMNAK |\r\n| 104  | **RUNet + R2Unet**                           | https://bit.ly/2Q4bIxG |\r\n| 105  | **VoxResNet**                                | https://bit.ly/32gLBWN |\r\n| 106  | **Unet++**                                   | https://bit.ly/3esShGV |\r\n| 107  | **H-DenseUnet**                              | https://bit.ly/3dN53kn |\r\n| 108  | **DUnet**                                    | https://bit.ly/3sPYrWS |\r\n| 109  | **MultiResUnet**                             | https://bit.ly/32J7Epr |\r\n| 110  | **Unet3+**                                   | https://bit.ly/3vj4lRX |\r\n| 111  | **VGGNet For Covid19**                       | https://bit.ly/3ewquW6 |\r\n| 112  | 𝗗𝗲𝗻𝘀𝗲-𝗚𝗮𝘁𝗲𝗱 𝗨-𝗡𝗲𝘁 (𝗗𝗚𝗡𝗲𝘁)                    | https://bit.ly/3tR67cM |\r\n| 113  | **Ki-Unet**                                  | https://bit.ly/3gD4wDK |\r\n| 114  | **Medical Transformer**                      | https://bit.ly/3dLw9Zf |\r\n| 115  | **Deep Snake- Instance Segmentation**        | https://bit.ly/3dQmdhm |\r\n| 116  | **BlendMask**                                | https://bit.ly/32LVXyf |\r\n| 117  | **CenterNet**                                | https://bit.ly/3aJrJQD |\r\n| 118  | **SRCNN**                                    | https://bit.ly/3t82eie |\r\n| 119  | **Swin Transformer**                         | https://bit.ly/2QMWxct |\r\n| 120  | **Polygon-RNN**                              | https://bit.ly/3ujEJ7D |\r\n| 121  | **PolyTransform**                            | https://bit.ly/3gT11ZZ |\r\n| 122  | **D2Det**                                    | https://bit.ly/3b2EDJL |\r\n| 123  | **PolarMask**                                | https://bit.ly/3uklSsO |\r\n| 124  | **FGN**                                      | https://bit.ly/3uiyyAl |\r\n| 125  | **Meta-SR**                                  | https://bit.ly/3ekFyr9 |\r\n| 126  | **Iterative Kernel Correlation**             | https://bit.ly/3xPGZp6 |\r\n| 127  | **SRFBN**                                    | https://bit.ly/2Qc1c7z |\r\n| 128  | **ODE**                                      | https://bit.ly/3w1K8k4 |\r\n| 129  | **SRNTT**                                    | https://bit.ly/2RNT9hS |\r\n| 130  | **Parallax Attention**                       | https://bit.ly/3tIr74x |\r\n| 131  | **3D Super Resolution**                      | https://bit.ly/3bliXJa |\r\n| 132  | **FSTRN**                                    | https://bit.ly/3uWJ8h7 |\r\n| 133  | **PointGroup**                               | https://bit.ly/2QfeKPP |\r\n| 134  | **3D-MPA**                                   | https://bit.ly/3bqz9J6 |\r\n| 135  | **Saliency Propagation**                     |                 https://bit.ly/3tXTvj4 |\r\n| 136  | **Libra R-CNN**                              | https://bit.ly/3hDytnt |\r\n| 137  | **SiamRPN++**                                | https://bit.ly/33TNjyi |\r\n| 138 | **LoFTR** | https://bit.ly/3eUtlJS |\r\n| 139 | **MZSR** | https://bit.ly/3ul5gAs |\r\n| 140 | **UCTGAN** | https://bit.ly/3fQg9ox |\r\n| 141 | **OccuSeg** | https://bit.ly/3bUJtta |\r\n| 142 | **LAPGAN** | https://bit.ly/3unOjW1 |\r\n| 143 | **TPN** | https://bit.ly/3vvyIoW |\r\n| 144 | **GTAD** | https://bit.ly/3c09yqK |\r\n| 145 | **SlowFast** | https://bit.ly/3fMrI0d |\r\n| 146 | **IDU** | https://bit.ly/2ROcIa5 |\r\n| 147 | **ATSS** | https://bit.ly/3hTIflC |\r\n| 148 | **Attention-RPN** | https://bit.ly/3oYescY |\r\n| 149 | **Aug-FPN**                                  | https://bit.ly/3fUbdzi |\r\n| 150 | **Hit-Detector** | https://bit.ly/3uGCLgB |\r\n| 151 | **MCN** | https://bit.ly/3ySpjtq |\r\n| 152 | **CentripetalNet** | https://bit.ly/2S1WNVB |\r\n| 153 | **ROAM** | https://bit.ly/34Ft8Ex |\r\n| 154 | **PF-NET(3D)** | https://bit.ly/2TzQiK9 |\r\n| 155 | **PointAugment** | https://bit.ly/3uMc8Hr |\r\n| 156 | **C-Flow** | https://bit.ly/3xgDlUn |\r\n| 157 | **RandLA-Net** | https://bit.ly/3fYajD9 |\r\n| 158 | **Total3DUnderStanding** | https://bit.ly/3v3jy9c |\r\n| 159 | **IF-Nets** | https://bit.ly/3v7XjPj |\r\n| 160 | **PerfectShape** | https://bit.ly/3za20vk |\r\n| 161 | **ACNe** | https://bit.ly/3gaJQSN |\r\n| 162 | **PQ-Net** | https://bit.ly/35dVPsm |\r\n| 163 | **SG-NN** | https://bit.ly/3iQ4yca |\r\n| 164 | **Cascade Cost Volume** | https://bit.ly/3gyZHtt |\r\n| 165 | **SketchGCN** | https://bit.ly/3pVoxI8 |\r\n| 166 | **Spektral (Graph Neural Network)** | https://bit.ly/3q2T079 |\r\n| 167 | **Graph Convolution Neural Network** | https://bit.ly/3gAkiNX |\r\n| 168 | **Fast Localized Spectral Filtering(Graph Kernel)** | https://bit.ly/3iRUEa0 |\r\n| 169 | **GraphSAGE** | https://bit.ly/3gCj9Xx |\r\n| 170 | **ARMA Convolution** | https://bit.ly/3qcubpC |\r\n| 171 | **Graph Attention Networks** | https://bit.ly/3h1gfKy |\r\n| 172 | **Axial-Deeplab** | https://bit.ly/3qiIF7l |\r\n| 173 | **Tide** | https://bit.ly/3j5evmh |\r\n| 174 | **SipMask** | https://bit.ly/3gMBoJE |\r\n| 175 | **UFO²** | https://bit.ly/2SVS2xA |\r\n| 176 | **SCAN** | https://bit.ly/2ThBv70 |\r\n| 177 | **AABO** : **Adaptive Anchor Box Optimization** | https://bit.ly/3qCSRaP |\r\n| 178 | **SimAug** | https://bit.ly/3dlV6tK |\r\n| 179 | **Instant-teaching** | https://bit.ly/3h0E2LU |\r\n| 180 | **Refinement Network for RGB-D** | https://bit.ly/3dtRh5O |\r\n| 181 | **Polka Lines** | https://bit.ly/3hlNbhd |\r\n| 182 | **HOTR** | https://bit.ly/3hsV44i |\r\n| 183 | **Soft-IntroVAE** | https://bit.ly/3jFozTk |\r\n| 184 | **ReXNet** | https://bit.ly/3r42WO9 |\r\n| 185 | **DiNTS** | https://bit.ly/3AQibii |\r\n| 186 | **Pose2Mesh** | https://bit.ly/3wFTORi |\r\n| 187 | **Keep Eyes on the Lane** | https://bit.ly/3wxs4hl |\r\n| 188 | **AssembleNet++** | https://bit.ly/3xAHhjf |\r\n| 189 | **SNE-RoadSeg** | https://bit.ly/3hyCEAL |\r\n| 190 | **AdvPC** | https://bit.ly/3i3dGrV |\r\n| 191 | **Eagle eye** | https://bit.ly/3e5Iqaz |\r\n| 192 | **Deep Hough Transform** | https://bit.ly/2UEFbAm |\r\n| 193 | **WeightNet** | https://bit.ly/3rfDSUL |\r\n| 194 | **StyleMAPGAN** | https://bit.ly/2URgPTO |\r\n| 195 | **PD-GAN** | https://bit.ly/3xQMCmM |\r\n| 196 | **Non-Local Sparse Attention** | https://bit.ly/3xJZbAd |\r\n| 197 | **TediGAN** | https://bit.ly/3wH67MZ |\r\n| 198 | **FedDG** | https://bit.ly/3zfKiGe |\r\n| 199 | **Auto-Exposure Fusion** | https://bit.ly/3y3F2W1 |\r\n| 200 | **Involution** | https://bit.ly/36Ksiaz |\r\n| 201 | **MutualNet** | https://bit.ly/3zhfd4N |\r\n| 202 | **Teachers do more than teach - Image to Image translation** | https://bit.ly/36RP28K |\r\n| 203 | **VideoMoCo** | https://bit.ly/3f6Pq7Z |\r\n| 204 | **ArtGAN** | https://bit.ly/3rvDCB9 |\r\n| 205 | **Vip-DeepLab** | https://bit.ly/3xmzmVX |\r\n| 206 | **PSConvolution** | https://bit.ly/3rEIgMY |\r\n| 207 | **Deep learning technique on Semantic Segmentation** | https://bit.ly/375hrID |\r\n| 208 | **Synthetic to Real** | https://bit.ly/3yfZSRO |\r\n| 209 | **Panoptic Segmentation** | https://bit.ly/376tbdA |\r\n| 210 | **HistoGAN** | https://bit.ly/3zSYyVD |\r\n| 211 | **Semantic Image Matting** | https://bit.ly/3s5ZD9F |\r\n| 212 | **Anchor-Free Person Search** | https://bit.ly/2VI0KAD |\r\n| 213 | **Spatial-Phase-Shallow-Learning** | https://bit.ly/3CDAl82 |\r\n| 214 | **LiteFlowNet3** | https://bit.ly/3yDILcO |\r\n| 215 | **EfficientNetv2** | https://bit.ly/3xAQsiE |\r\n| 216 | **CBNETv2** | https://bit.ly/3s3ptvb |\r\n| 217 | **PerPixel Classification** | https://bit.ly/3lOomyg |\r\n| 218 | **Kaleido-BERT** | https://bit.ly/3ywh2Lf |\r\n| 219 | **DARKGAN** | https://bit.ly/3lTW05J |\r\n| 220 | **PPDM** | https://bit.ly/3lPgjBt |\r\n| 221 | **SEAN** | https://bit.ly/3yOUJ3L |\r\n| 222 | **Closed-Loop Matters** | https://bit.ly/3CzBnlq |\r\n| 223 | **Elastic Graph Neural Network** | https://bit.ly/3jket9S |\r\n| 224 | **Deep Imbalance Regression** | https://bit.ly/3yn0Ue3 |\r\n| 225 | **PIPAL** - Image Quality Assessment | https://bit.ly/3gCliSx |\r\n| 226 | **Mobile-Former** | https://bit.ly/3kxCSbm |\r\n| 227 | **Rank and Sort Loss** | https://bit.ly/3sPQt1s |\r\n| 228 | **Room Classification using Graph Neural Network** | https://bit.ly/3gD8Odv |\r\n| 229 | **Pyramid Vision Transformer** | https://bit.ly/3zmod9h |\r\n| 230 | **EigenGAN** | https://bit.ly/3BfdIVO |\r\n| 231 | **GNeRF** | https://bit.ly/3mD3kTR |\r\n| 232 | **DetCo** | https://bit.ly/3sQiRk9 |\r\n| 233 | **DERT with Special Modulated Co-Attention**                 | https://bit.ly/3sPQ5jw |\r\n|      | **Residual Attention** | https://bit.ly/3yni4bJ |\r\n| 235 | **MG-GAN** | https://bit.ly/3mD30o7 |\r\n| 236 | **Adaptable GAN Encoders** | https://bit.ly/3yh4XJ3 |\r\n| 237 | **AdaAttN** | https://bit.ly/3BepKPa |\r\n| 238 | **Conformer** | https://bit.ly/3gCkj4N |\r\n| 239 | **YOLOP** | https://bit.ly/3BicysB |\r\n| 240 | **VMNet** | https://bit.ly/3k73jFZ |\r\n| 241 | **Airbert** | https://bit.ly/3nvcrGs |\r\n| 242 | 𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗥-𝗖𝗡𝗡 | https://bit.ly/397Zius |\r\n| 243 | **Battle of Network Structure** | https://bit.ly/2XcHbB0 |\r\n| 244 | **InSeGAN** | https://bit.ly/3z9wyMF |\r\n| 245 | **Efficient Person Search** | https://bit.ly/3CpbZOr |\r\n| 246 | **DeepGCNs** | https://bit.ly/3AevSHg |\r\n| 247 | **GroupFormer** | https://bit.ly/3lqzm2Y |\r\n| 248 | **SLIDE** | https://bit.ly/3hwpiEp |\r\n| 249 | **Super Neuron** | https://bit.ly/3zkXE3D |\r\n| 250 | **SOTR** | https://bit.ly/3hvqCYl |\r\n| 251 | **Survey : Instance Segmentation** | https://bit.ly/3k90xQB |\r\n| 252 | **SO-Pose** | https://bit.ly/3C56KD8 |\r\n| 253 | **CANet** | https://bit.ly/2XlDKZ2 |\r\n| 254 | **XVFI** | https://bit.ly/3lrOpcZ |\r\n| 255 | **TxT** | https://bit.ly/3tGFlEH |\r\n| 256 | **ConvMLP** | https://bit.ly/2XlE8Xu |\r\n| 257 | **Cross Domain Contrastive Learning** | https://bit.ly/3tDb2id |\r\n| 258 | **OS2D: One Stage Object Detection** | https://bit.ly/3ufnEMD |\r\n| 259 | **PointManifoldCut** | https://bit.ly/3CKvAIL |\r\n| 260 | **Large Scale Facial Expression Dataset** | https://bit.ly/2ZqtT4V |\r\n| 261 | **Graph-FPN** | https://bit.ly/2XH8T9f |\r\n| 262 | **3D Shape Reconstruction** | https://bit.ly/2XTe9aq |\r\n| 263 | **Open Graph Benchmark Dataset** | https://bit.ly/3ET2Lfl |\r\n| 264 | **ShiftAddNet** | https://bit.ly/3i6eb5C |\r\n| 265 | **WatchOut! Motion Blurring the vision of your DNN** | https://bit.ly/3CKTzrw |\r\n| 266 | **Rethinking Learnable Tree Filter** | https://bit.ly/3zHfPAC |\r\n| 267 | **Neuron Merging** | https://bit.ly/39DwLNS |\r\n| 268 | **Distance IOU Loss** | https://bit.ly/3i7Zj6z |\r\n| 269 | **Deep Imitation learning** | https://bit.ly/3AzGVd6 |\r\n| 270 | **Pixel Level Cycle Association** | https://bit.ly/3iTZMK6 |\r\n| 271 | **Deep Model Fusion** | https://bit.ly/2YK45kl |\r\n| 272 | **Object Representation Network** | https://bit.ly/3BA0mnE |\r\n| 273 | **HOI Analysis** | https://bit.ly/3FH2Key |\r\n| 274 | **Deep Equilibrium Models** | https://bit.ly/3FDH2IB |\r\n| 275 | **Sampling from k-DPP** | https://bit.ly/3BAyRuc |\r\n| 276 | **Rotated Binary Neural Network** | https://bit.ly/3mIuYx3 |\r\n| 277 | **PP-LCNet** - **LightCNN** | https://bit.ly/3v1Zh5H |\r\n| 278 | **MC-Net+** | https://bit.ly/3v5tYqk |\r\n| 279 | **Fake it till you make it** | https://bit.ly/3AyGTSQ |\r\n| 280 | **Enformer** | https://bit.ly/3AAdCr9 |\r\n| 281 | **VideoClip** | https://bit.ly/3mOueGu |\r\n| 282 | **Moving Fashion** | https://bit.ly/3jdvAtN |\r\n| 283 | **Convolution to Transformer** | https://bit.ly/3v5yy8f |\r\n| 284 | **HeadGAN** | https://bit.ly/3BLzRvm |\r\n| 285 | **Focal Transformer** | https://bit.ly/3lvCYSI |\r\n| 286 | **StyleGAN3** | https://bit.ly/3kvFPKw |\r\n| 287 | **3Detr:3D Object Detection** | https://bit.ly/3Hfk6A8 |\r\n| 288 | **Do Self-Supervised and Supervised Methods Learn Similar Visual Representations?** | https://bit.ly/3kyWM6H |\r\n| 289 | **Back to the Features** | https://bit.ly/3kvsxh3 |\r\n| 290 | **Anticipative Video Transformer** | https://bit.ly/30mADl2 |\r\n| 291 | **Attention Meets Geometry** | https://bit.ly/3kweSpZ |\r\n| 292 | **DeepMoCaP:** Deep Optical Motion Capture | https://bit.ly/30mjTdT |\r\n| 293 | **TrOCR: Transformer-based Optical Character Recognition** | https://bit.ly/3DqenW5 |\r\n| 294 | **Moving Fashion** | https://bit.ly/2YGtjA1 |\r\n| 295 | **StyleNeRF** | https://bit.ly/31W4Mbz |\r\n| 296 | **ECA-Net: :Efficient Channel Attention** | https://bit.ly/3n92i1s |\r\n| 297 | **Inferring High Resolution Traffic Accident risk maps** | https://bit.ly/3HgovD6 |\r\n| 298 | **Bias Loss: For Mobile Neural Network** | https://bit.ly/3qvBPNO |\r\n| 299 | **ByteTrack: Multi-Object Tracking** | https://bit.ly/3c3l7wQ |\r\n| 300 | **Non-Deep Network** | https://bit.ly/3qwZwoV |\r\n| 301 | **Temporal Attentive Covariance** | https://bit.ly/3ontCbP |\r\n| 302 | **Plan-then-generate: Controlled Data to Text Generation** | https://bit.ly/3DcbsA6 |\r\n| 303 | **Dynamic Visual Reasoning** | https://bit.ly/31Q4BhP |\r\n| 304 | **MedMNIST: Medical MNIST Dataset** | https://bit.ly/3qxuqxq |\r\n| 305 | **Colossal-AI: A PyTorch-Based Deep Learning System For Large-Scale Parallel Training** | https://bit.ly/3wG6Xv8 |\r\n| 306 | **Recursively Embedded Atom Neural Network(REANN)** | https://bit.ly/3F1JKqe |\r\n| 307 | **PolyTrack: for fast multi-object tracking and segmentation** | https://bit.ly/3DeBmmS |\r\n| 308 | **Can contrastive learning avoid shortcut solutions?** | https://bit.ly/3wHJIk9 |\r\n| 309 | **ProjectedGAN:  To Improve Image Quality** | https://bit.ly/30hw8Zm |\r\n| 310 | **Arch-Net:  A Family Of Neural Networks Built With Operators To Bridge The Gap ** | https://bit.ly/3oFOCef |\r\n| 311 | **PP-ShiTu:A Practical Lightweight Image Recognition System** | https://bit.ly/3naurFw |\r\n| 312 | **EditGAN** | https://bit.ly/30gYd2Z |\r\n| 313 | **Panoptic 3D Scene Segmentation** | https://bit.ly/3caSvla |\r\n| 314 | **PARP: Improve the Efficiency of NN** | https://bit.ly/3DakTjt |\r\n| 315 | **WORD: Organ Segmentation Dataset** | https://bit.ly/3qv5OW2 |\r\n| 316 | **DenseULearn** | https://bit.ly/3ohRiyi |\r\n| 317 | **Does Thermal data make the detection systems more reliable?** | https://bit.ly/3sQgTSO |\r\n| 318 | **MADDNESS: Approximate Matrix Multiplication (AMM)** | https://bit.ly/3zgVIL4 |\r\n| 319 | **Deceive D: Adaptive Pseudo Augmentation** | https://bit.ly/3sIG6yA |\r\n| 320 | **OadTR** | https://bit.ly/3JsUHUF |\r\n| 321 | **OnePassImageNet** | https://bit.ly/3sKL6Ti |\r\n| 322 | **Image-specific Convolutional Kernel Modulation for Single Image Super-resolution** | https://bit.ly/3FUpA20 |\r\n| 323 | **TransMix** | https://bit.ly/3EH93gH |\r\n| 324 | **PytorchVideo** | https://bit.ly/3JvgDP7 |\r\n| 325 | **MetNet-2** | https://bit.ly/3sMZb2M |\r\n| 326 | **Unsupervised deep learning identifies semantic disentanglement** | https://bit.ly/3JyAwVi |\r\n| 327 | **Story Visualization** | https://bit.ly/3qB554i |\r\n| 328 | **MetaFormer** | https://bit.ly/3sLBebP |\r\n| 329 | **GauGAN2** | https://bit.ly/3pGrIVH |\r\n| 330 | **SciGAP** | https://bit.ly/3EB7e4U |\r\n| 331 | **Generative Flow Networks (GFlowNets)** | https://bit.ly/3Jv9YEz |\r\n| 332 | **Ensemble Inversion** | https://bit.ly/3ECwbg9 |\r\n| 333 | **SAVi** | https://bit.ly/3eF6txe |\r\n| 334 | **Digital Optical Neural Network** | https://bit.ly/3EI07rh |\r\n| 335 | **Image-Generation Research With Manifold Matching Via Metric Learning** | https://bit.ly/3FUomnq |\r\n| 336 | **GHN-2(Graph HyperNetworks)** | https://bit.ly/3qzc5yB |\r\n| 337 | **NeatNet** | https://bit.ly/3sLY17r |\r\n| 338 | **NeuralProphet** | https://bit.ly/3JrUK38 |\r\n| 339 | **Background Activation Suppression for Weakly Supervised Object Detection** | https://bit.ly/3Jvyzt2 |\r\n| 340 | **Learning to Detect Every Thing in an Open World** | https://bit.ly/3mKxOTc |\r\n| 341 | **PoolFormer** | https://bit.ly/3qFHNtS |\r\n| 342 | **GLIP** | https://bit.ly/3mK3bgx |\r\n| 343 | **PHALP** | https://bit.ly/3eJJvEV |\r\n| 344 | **PixMix** | https://bit.ly/3Hqh77m |\r\n| 345 | **CodeNet** | https://bit.ly/32RPx3X |\r\n| 346 | **GANgealing** | https://bit.ly/3EIkO6k |\r\n| 347 | **Semantic Diffusion Guidance** | https://bit.ly/3JsNzI3 |\r\n| 348 | **TokenLearner** | https://bit.ly/3mLG4lM |\r\n| 349 | **Temporal Fusion Transformer (TFT)** | https://bit.ly/3JuHcno |\r\n| 350 | **HiClass: Evaluation Metrics for Local Hierarchical Classification** | https://bit.ly/3JHmn8H |\r\n| 351 | **Stable Long Term Recurrent Video Super Resolution** | https://bit.ly/3qFlPHl |\r\n| 352 | **AdaViT** | https://bit.ly/3eDASMj |\r\n| 353 | **Few-Shot Learner (FSL)** | https://bit.ly/3ELOOym |\r\n| 354 | **Exemplar Transformers** | https://bit.ly/3qzJE3C |\r\n| 355 | **StyleSwin** | https://bit.ly/3HqkCe4 |\r\n| 356 | **RepMLNet** | https://bit.ly/32DxbUu |\r\n| 357 | **2 Stage Unet** | https://bit.ly/3JGjIMq |\r\n| 358 | **Untrained Deep NN** | https://bit.ly/3JplL7r |\r\n| 359 | **SeMask** | https://bit.ly/3zfouM8 |\r\n| 360 | **JoJoGAN** | https://bit.ly/31gl9Qi |\r\n| 361 | **ELSA** | https://bit.ly/3mLWScb |\r\n| 362 | **PRIME** | https://bit.ly/3FI14RZ |\r\n| 363 | **GLIDE** | https://bit.ly/31ixB20 |\r\n| 364 | **StyleGAN-V** | https://bit.ly/3Jvx91G |\r\n| 365 | **SLIP: Self-supervision meets Language-Image Pre-training** | https://bit.ly/3qAjL3r |\r\n| 366 | **SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos** | https://bit.ly/3tYNxlp |\r\n| 367 | **Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results** | https://bit.ly/3tZFyEQ |\r\n| 368 | **PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability** | https://bit.ly/3LCKENk |\r\n| 369 | **Vision Transformer with Deformable Attention** | https://bit.ly/3tY3s3k |\r\n| 370 | **A Transformer-Based Siamese Network for Change Detection** | https://bit.ly/3DxPYP5 |\r\n| 371 | **Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention** | https://bit.ly/3qRsTle |\r\n| 372 | **SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection** | https://bit.ly/3tXduls |\r\n| 373 | **HyperionSolarNet: Solar Panel Detection from Aerial Images** | https://bit.ly/35v2rX6 |\r\n| 374 | **Realistic Full-Body Anonymization with Surface-Guided GANs** | https://bit.ly/3DwBNd4 |\r\n| 375 | **Generalized Category Discovery** | https://bit.ly/3IZ1HaC |\r\n| 376 | **KerGNNs: Interpretable Graph Neural Networks with Graph Kernels** | https://bit.ly/3DtWtlU |\r\n| 377 | **Optimization Planning for 3D ConvNets** | https://bit.ly/3K38e5p |\r\n| 378 | **gDNA: Towards Generative Detailed Neural Avatars** | https://bit.ly/3DEtFHC |\r\n| 379 | **SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps** | https://bit.ly/3NIieTA |\r\n| 380 | **HYDLA: Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning** | https://bit.ly/379dy8v |\r\n| 381 | **HardBoost: Boosting Zero-Shot Learning with Hard Classes** | https://bit.ly/379diX5 |\r\n| 382 | **DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images** | https://bit.ly/3Lu0UzU |\r\n| 383 | **Q-ViT: Fully Differentiable Quantization for Vision Transformer** | https://bit.ly/3qXv9Ym |\r\n| 384 | **SPAMs: Structured Implicit Parametric Models** | https://bit.ly/3iU95cL |\r\n| 385 | **GeoFill: Reference-Based Image Inpainting of Scenes with Complex Geometry** | https://bit.ly/3qUwCP6 |\r\n| 386 | **Improving language models by retrieving from trillions of tokens** | https://bit.ly/37aKsG5 |\r\n| 387 | **StylEx finds and visualizes disentangled attributes that affect a classifier automatically.** | https://bit.ly/3qYwYEf |\r\n| 388 | **‘ReLICv2’: Pushing The Limits of Self-Supervised ResNet** | https://bit.ly/3JZXy7C |\r\n| 389 | **‘Detic’: A Method to Detect Twenty-Thousand Classes using Image-Level Supervision** | https://bit.ly/3iRtsqZ |\r\n| 390 | **Momentum Capsule Networks** | https://bit.ly/3NFDv0j |\r\n| 391 | **RelTR: Relation Transformer for Scene Graph Generation** | https://bit.ly/3iVBWgB |\r\n| 392 | **Transformer based SAR Images Despecking** | https://bit.ly/3qWeILH |\r\n| 393 | **ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation** | https://bit.ly/3wWGY4T |\r\n| 394 | **VRT: A Video Restoration Transformer** | https://bit.ly/3K44YXw |\r\n| 395 | **You Only Cut Once: Boosting Data Augmentation with a Single Cut** | https://bit.ly/36L8pDW |\r\n| 396 | **StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets** | https://bit.ly/3iRlEp8 |\r\n| 397 | **The KFIoU Loss for Rotated Object Detection** | https://bit.ly/3NHUL5e |\r\n| 398 | **The Met Dataset: Instance Level Recognition** | https://bit.ly/3K7lPJ2 |\r\n| 399 | **Alphacode: a System that can  compete at average human level** | https://bit.ly/3qXIIH5 |\r\n| 400 | **Third Time's the Charm? Image and Video Editing with StyleGAN3** | https://bit.ly/35vAoqx |\r\n| 401 | **NeuralFusion: Online Depth Fusion in Latent Space** | https://bit.ly/3uFaysA |\r\n| 402 | **VOS: Learning what you don't know by VIRTUAL OUTLIER SYNTHESIS** | https://bit.ly/3uPG9rG |\r\n| 403 | **Self-Conditioned Generative Adversarial Networks for Image Editing** | https://bit.ly/3tX8m0u |\r\n| 404 | **TransformNet: Self-supervised representation learning through predicting geometric transformations** | https://bit.ly/3uOCfPM |\r\n| 405 | **YOLOv7 - Framework Beyond Detection** | https://bit.ly/3wXU81y |\r\n| 406 | **F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization** | https://bit.ly/3DzhFXU |\r\n| 407 | **Block-NeRF: Scalable Large Scene Neural View Synthesis** | https://bit.ly/3LyELk5 |\r\n| 408 | **Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition** | https://bit.ly/375C76y |\r\n| 409 | **COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets** | https://bit.ly/3NCK6bZ |\r\n| 410 | **ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification** | https://bit.ly/3uJuMBz |\r\n| 411 | **Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges** | https://bit.ly/388imeT |\r\n| 412 | **How Do Vision Transformers Work?** | https://bit.ly/3NE1mO2 |\r\n| 413 | **Mirror-Yolo: An attention-based instance segmentation and detection model for mirrors** | https://bit.ly/3LBS96P |\r\n| 414 | **PENCIL: Deep Learning with Noisy Labels** | https://bit.ly/3iXvHc4 |\r\n| 415 | **VLP: A Survey on Vision-Language Pre-training** | https://bit.ly/3J0v2RZ |\r\n| 416 | **Visual Attention Network** | https://bit.ly/3Dt7rbv |\r\n| 417 | **GroupViT: Semantic Segmentation Emerges from Text Supervision** | https://bit.ly/3NQv7eG |\r\n| 418 | **Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis** | https://bit.ly/373xs4T |\r\n| 419 | **End to End Cascaded Image De-raining and Object Detetion NN** | https://bit.ly/375PLGw |\r\n| 420 | **Level-K to Nash Equilibrium** | https://bit.ly/3NFRX8t |\r\n| 421 | **Machine Learning for Mechanical Ventilation Control** | https://bit.ly/3JZCMEV |\r\n| 422 | **The effect of fatigue on the performance of online writer recognition** | https://bit.ly/3wXSSLS |\r\n| 423 | **State-of-the-Art in the Architecture, Methods and Applications of StyleGAN** | https://bit.ly/3iRjl5s |\r\n| 424 | **Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation** | https://bit.ly/3v5XZXR |\r\n| 425 | **Self-supervised Transformer for Deepfake Detection** | https://bit.ly/3tXtUdk |\r\n| 426 | **CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size** | https://bit.ly/3LxkrQa |\r\n| 427 | **TCTrack: Temporal Contexts for Aerial Tracking** | https://bit.ly/3uM5O4B |\r\n| 428 | **LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction** | https://bit.ly/3uOfKe0 |\r\n| 429 | **HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening** | https://bit.ly/35tRV2j |\r\n| 430 | **ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization** | https://bit.ly/3LwoMmy |\r\n| 431 | **MLSeg: Image and Video Segmentation** | https://bit.ly/38p9iCN |\r\n| 432 | **Image Steganography based on Style Transfer** | https://bit.ly/3DJHLaN |\r\n| 433 | **GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains** | https://bit.ly/3JYPrIg |\r\n| 434 | **AGCN: Augmented Graph Convolutional Network** | https://bit.ly/3DwZrWN |\r\n| 435 | **StyleBabel: Artistic Style Tagging and Captioning** | https://bit.ly/3j1Klit |\r\n| 436 | **ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI** | https://bit.ly/38maN4z |\r\n| 437 | **InsetGAN for Full-Body Image Generation** | https://bit.ly/3Dsu9At |\r\n| 438 | **Implicit Feature Decoupling with Depthwise Quantization** | https://bit.ly/3K1mxaA |\r\n| 439 | **Bamboo: Building Mega-Scale Vision Dataset** | https://bit.ly/3wVPalD |\r\n| 440 | **TensoRF: Tensorial Radiance Fields** | https://bit.ly/3iWAFWI |\r\n| 441 | **FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition** | https://bit.ly/3NCHTxd |\r\n| 442 | **One-Shot Adaptation of GAN in Just One CLIP** | https://bit.ly/36NOPab |\r\n| 443 | **SHREC 2021: Classification in cryo-electron tomograms** | https://bit.ly/3iSXpqv |\r\n| 444 | **MaskGIT: Masked Generative Image Transformer** | https://bit.ly/3qSQz8I |\r\n| 445 | **Detection, Recognition, and Tracking: A Survey** | https://bit.ly/378G8qw |\r\n| 446 | **Mixed Differential Privacy** | https://bit.ly/3IZ0MGU |\r\n| 447 | **Mixed DualStyleGAN** | https://bit.ly/3wTyAmD |\r\n| 448 | **BigDetection** | https://bit.ly/3DuZSRk |\r\n| 449 | **Feature visualization for convolutional neural network** | https://bit.ly/3Dwf6FJ |\r\n| 450 | **AutoAvatar** | https://bit.ly/38m9ClF |\r\n| 451 | **A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction** | https://bit.ly/3Dz1idF |\r\n| 452 | **StyleT2I** | https://bit.ly/35u5Wx0 |\r\n| 453 | **L^3U-net** | https://bit.ly/3iTOq8r |\r\n| 454 | **Balanced MSE** | https://bit.ly/3rxt7yo |\r\n| 455 | **BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers** | https://bit.ly/36m3HfC |\r\n| 456 | **TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing** | https://bit.ly/3JQKZKS |\r\n| 457 | **On the Importance of Asymmetry for Siamese Representation Learning** | https://bit.ly/3JNgcyt |\r\n| 458 | **On One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks** | https://bit.ly/3uQTC3P |\r\n| 459 | **Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth** | https://bit.ly/3KWT6a4 |\r\n| 460 | **Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection** | https://bit.ly/3L8a59H |\r\n| 461 | **DaViT: Dual Attention Vision Transformers** | https://bit.ly/3Engc7e |\r\n| 462 | **SPAct: Self-supervised Privacy Preservation for Action Recognition** | https://bit.ly/3KTNvRW |\r\n| 463 | **Class-Incremental Learning with Strong Pre-trained Models** | https://bit.ly/3MdlcOq |\r\n| 464 | **RBGNet: Ray-based Grouping for 3D Object Detection by Center for Data Science** | https://bit.ly/3EqkydH |\r\n| 465 | **Event Transformer** | https://bit.ly/3KUsMxc |\r\n| 466 | **ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension** | https://bit.ly/3M6RgDE |\r\n| 467 | **A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research** | https://bit.ly/3xAyqRj |\r\n| 468 | **Simple Baselines for Image Restoration** | https://bit.ly/3vt4tjB |\r\n| 469 | **Masked Siamese Networks for Label-Efficient Learning** | https://bit.ly/3viEs6s |\r\n| 470 | **Neighborhood Attention Transformer** | https://bit.ly/3jNExK3 |\r\n| 471 | **TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation** | https://bit.ly/3M3EA0K |\r\n| 472 | **MVSTER: Epipolar Transformer for Efficient Multi-View Stereo** | https://bit.ly/3MaDTCR |\r\n| 473 | **Temporally Efficient Vision Transformer for Video Instance Segmentation** | https://bit.ly/3w6xkf3 |\r\n| 474 | **EditGAN: High-Precision Semantic Image Editing** | https://bit.ly/3yx2JJ2 |\r\n| 475 | **CenterNet++ for Object Detection** | https://bit.ly/3woxrBG |\r\n| 476 | **A case for using rotation invariant features in state of the art feature matchers** | https://bit.ly/3kZ1x9A |\r\n| 477 | **WebFace260M: A Benchmark for Million-Scale Deep Face Recognition** | https://bit.ly/3w2T3Vd |\r\n| 478 | **JIFF: Jointly-aligned Implicit Face Function for High-Quality Single View Clothed Human Reconstruction** | https://bit.ly/3N9Me9U |\r\n| 479 | **Image Data Augmentation for Deep Learning: A Survey** | https://bit.ly/3PfC1uA |\r\n| 480 | **StyleGAN-Human: A Data-Centric Odyssey of Human Generation** | https://bit.ly/3PqV710 |\r\n| 481 | **Few-shot Head Swapping In The Wild Secrets Revealed By Department Of Computer Vision Technology (vis)** | https://bit.ly/3w7xm6c |\r\n| 482 | **CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP** | https://bit.ly/3N3cEKu |\r\n| 483 | **HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling** | https://bit.ly/3Nqnevx |\r\n| 484 | **Generative Adversarial Networks for Image Super-Resolution: A Survey** | https://bit.ly/39jyL0U |\r\n| 485 | **CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification** | https://bit.ly/3N7Qd6V |\r\n| 486 | **C3-STISR: Scene Text Image Super-resolution with Triple Clues** | https://bit.ly/3l1352C |\r\n| 487 | **Barbershop: GAN-based Image Compositing using Segmentation Masks** | https://bit.ly/39hus6d |\r\n| 488 | **DANBO: Disentangled Articulated Neural Body Representations** | https://bit.ly/3LkqWp3 |\r\n| 489 | **BlobGAN: Spatially Disentangled Scene Representations** | https://bit.ly/3sufEYz |\r\n| 490 | **Text to artistic image generation** | https://bit.ly/3w6wzmd |\r\n| 491 | **Sequencer: Deep LSTM for Image Classification** | https://bit.ly/3sulPvT |\r\n| 492 | **IVY: An Open-Source Tool To Make Deep Learning Code Compatible Across Frameworks** | https://bit.ly/3M6MbvJ |\r\n| 493 | **Introspective Deep Metric Learning** | https://bit.ly/3w2pZ02 |\r\n| 494 | **KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints** | https://bit.ly/3wnRhwF |\r\n| 495 | **GraphWorld: A Methodology For Analyzing The Performance Of GNN Architectures On Millions Of Synthetic Benchmark Datasets** | https://bit.ly/3PUQexk |\r\n| 496 | **Group R-CNN for Weakly Semi-supervised Object Detection with Points** | https://bit.ly/3zfvU3W |\r\n| 497 | **Few-Shot Head Swapping in the Wild** | https://bit.ly/3xapGkn |\r\n| 498 | **StyLandGAN: A StyleGAN based Landscape Image Synthesis using Depth-map** | https://bit.ly/3GKX4Bi |\r\n| 499 | **Spiking Approximations of the MaxPooling Operation in Deep SNNs** | https://bit.ly/3GLp7AG |\r\n| **500** | **Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization** | https://bit.ly/3NTGsJQ |\r\n\r\n***Thanks for Reading🎉🎉🎉🎉***\r\n\r\n----\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashishpatel26%2F365-Days-Computer-Vision-Learning-Linkedin-Post","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fashishpatel26%2F365-Days-Computer-Vision-Learning-Linkedin-Post","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashishpatel26%2F365-Days-Computer-Vision-Learning-Linkedin-Post/lists"}