Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post

365 Days Computer Vision Learning Linkedin Post
https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post
computer-vision cvpr cvpr2018 cvpr2019 cvpr2020 deep-learning eccv eccv-2018 eccv2019 eccv2020 iclr iclr2018 iclr2019 iclr2020 iclr2021 jmlr linkedin
Last synced: 3 months ago
JSON representation
365 Days Computer Vision Learning Linkedin Post
Host: GitHub
URL: https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post
Owner: ashishpatel26
Created: 2021-01-22T08:19:38.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2022-06-03T05:43:52.000Z (about 2 years ago)
Last Synced: 2023-10-20T17:46:48.323Z (9 months ago)
Topics: computer-vision, cvpr, cvpr2018, cvpr2019, cvpr2020, deep-learning, eccv, eccv-2018, eccv2019, eccv2020, iclr, iclr2018, iclr2019, iclr2020, iclr2021, jmlr, linkedin
Homepage:
Size: 233 KB
Stars: 425
Watchers: 25
Forks: 136
Open Issues: 1
Metadata Files:
- Readme: README.md
Lists

my-awesome-stars - ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post - 365 Days Computer Vision Learning Linkedin Post (Others)
README

        ## 365 Days Computer Vision Learning LinkedIn Post

Follow me on LinkedIn : https://www.linkedin.com/in/ashishpatel2604/

![](https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post/blob/main/poster.gif)

| Days | Topic                                        | Post Link              |

| ---- | -------------------------------------------- | ---------------------- |

| 1    | **EfficientDet**                             | https://bit.ly/362NWHa |

| 2    | **Yolact++**                                 | https://bit.ly/3o5OaU3 |

| 3    | **YOLO Series**                              | https://bit.ly/3650LAJ |

| 4    | **Detr**                                     | https://bit.ly/39S5F57 |

| 5    | **Vision Transformer**                       | https://bit.ly/39UMHLd |

| 6    | **Dynamic RCNN**                             | https://bit.ly/3939gy5 |

| 7    | **DeiT: (Data-efficient image Transformer)** | https://bit.ly/363ZABt |

| 8    | **Yolov5**                                   | https://bit.ly/39QHTXq |

| 9    | **DropBlock**                                | https://bit.ly/3sM4TiG |

| 10   | **FCN**                                      | https://bit.ly/3iE9U8C |

| 11   | **Unet**                                     | https://bit.ly/3izdbG2 |

| 12   | **RetinaNet**                                | https://bit.ly/3o5NrlN |

| 13   | **SegNet**                                   | https://bit.ly/3qIauVz |

| 14   | **CAM**                                      | https://bit.ly/2Y2I8ZR |

| 15   | **R-FCN**                                    | https://bit.ly/3iCKsQL |

| 16   | **RepVGG**                                   | https://bit.ly/2Y2pGjV |

| 17   | **Graph Convolution Network**                | https://bit.ly/2LS9RK8 |

| 18   | **DeconvNet**                                | https://bit.ly/2Mhwzes |

| 19   | **ENet**                                     | https://bit.ly/2Y2HgEz |

| 20   | **Deeplabv1**                                | https://bit.ly/3o7Utqn |

| 21   | **CRF-RNN**                                  | https://bit.ly/2Y5nsR4 |

| 22   | **Deeplabv2**                                | https://bit.ly/2Y9DgSx |

| 23   | **DPN**                                      | https://bit.ly/363Cye2 |

| 24   | **Grad-CAM**                                 | https://bit.ly/3iF006q |

| 25   | **ParseNet**                                 | https://bit.ly/3oesFk5 |

| 26   | **ResNeXt**                                  | https://bit.ly/2M2sXxe |

| 27   | **AmoebaNet**                                | https://bit.ly/2YgRIbN |

| 28   | **DilatedNet**                               | https://bit.ly/2M9fuDS |

| 29   | **DRN**                                      | https://bit.ly/2KXVmUH |

| 30   | **RefineNet**                                | https://bit.ly/3cpCBVq |

| 31   | **Preactivation-Resnet**                     | https://bit.ly/2MJtgwQ |

| 32   | **SqueezeNet**                               | https://bit.ly/3cv3Ca0 |

| 33   | **FractalNet**                               | https://bit.ly/3pSv712 |

| 34   | **PolyNet**                                  | https://bit.ly/3atCQfJ |

| 35   | **DeepSim(Image Quality Assessment)**        | https://bit.ly/3oKJGTi |

| 36   | **Residual Attention Network**               | https://bit.ly/3cIjupL |

| 37   | **IGCNet / IGCV**                            | https://bit.ly/36LRfTo |

| 38   | **Resnet38**                                 | https://bit.ly/2N7tpKL |

| 39   | **SqueezeNext**                              | https://bit.ly/3cSev5W |

| 40   | **Group Normalization**                      | https://bit.ly/3ryNxEI |

| 41   | **ENAS**                                     | https://bit.ly/2LB6pDC |

| 42   | **PNASNet**                                  | https://bit.ly/3tIX6mx |

| 43   | **ShuffleNetV2**                             | https://bit.ly/2Zb3xAM |

| 44   | **BAM**                                      | https://bit.ly/3b67xb2 |

| 45   | **CBAM**                                     | https://bit.ly/3plxHvJ |

| 46   | **MorphNet**                                 | https://bit.ly/3rWzcSM |

| 47   | **NetAdapt**                                 | https://bit.ly/2NtlFmE |

| 48   | **ESPNetv2**                                 | https://bit.ly/3jWVoJv |

| 49   | **FBNet**                                    | https://bit.ly/3k1PXZL |

| 50   | **HideandSeek**                              | https://bit.ly/3qELCP0 |

| 51   | **MR-CNN & S-CNN**                           | https://bit.ly/2Zw6QTf |

| 52   | **ACoL: Adversarial Complementary Learning** | https://bit.ly/3qKFNiU |

| 53   | **CutMix**                                   | https://bit.ly/2Nt5shI |

| 54   | **ADL**                                      | https://bit.ly/3qNeFQm |

| 55   | **SAOL**                                     | https://bit.ly/2NVuBBs |

| 56   | **SSD**                                      | https://bit.ly/37PWpyo |

| 57   | **NOC**                                      | https://bit.ly/3uBrZJJ |

| 58   | **G-RMI**                                    | https://bit.ly/3kJDlap |

| 59   | **TDM**                                      | https://bit.ly/3dV5zgN |

| 60   | **DSSD**                                     | https://bit.ly/3q6EHg8 |

| 61   | **FPN**                                      | https://bit.ly/2OewZn0 |

| 62   | **DCN**                                      | https://bit.ly/3e3G4Kg |

| 63   | **Light-Head-RCNN**                          | https://bit.ly/388rtcT |

| 64   | **Cascade RCNN**                             | https://bit.ly/3uUDlZz |

| 65   | **MegNet**                                   | https://bit.ly/3bkNvuM |

| 66   | **StairNet**                                 | https://bit.ly/3bluE2P |

| 67   | **ImageNet Rethinking**                      | https://bit.ly/3bqBfZZ |

| 68   | **ERFNet**                                   | https://bit.ly/2OxgC5c |

| 69   | **LayerCascade**                             | https://bit.ly/3qzWdd8 |

| 70   | **IDW-CNN**                                  | https://bit.ly/3letEAY |

| 71   | **DIS**                                      | https://bit.ly/3vi3xh3 |

| 72   | **SDN**                                      | https://bit.ly/3lftn0k |

| 73   | **ResNet-DUC-HDC**                           | https://bit.ly/3lmdhlN |

| 74   | **Deeplabv3+**                               | https://bit.ly/3lfSRuR |

| 75   | **AutoDeeplab**                              | https://bit.ly/2P14kSF |

| 76   | **c3**                                       | https://bit.ly/3qX0yqK |

| 77   | **DRRN**                                     | https://bit.ly/3ltkWP9 |

| 78   | **BR²Net**                                   | https://bit.ly/3f0jGlI |

| 79   | **SDS**                                      | https://bit.ly/3f0CZLw |

| 80   | **AdderNet**                                 | https://bit.ly/3sfMdYa |

| 81   | **HyperColumn**                              | https://bit.ly/3vV7Jn5 |

| 82   | **DeepMask**                                 | https://bit.ly/3cY2RVR |

| 83   | **SharpMask**                                | https://bit.ly/3rg0h2r |

| 84   | **MultipathNet**                             | https://bit.ly/31fcTMR |

| 85   | **MNC**                                      | https://bit.ly/39rRXqj |

| 86   | **InstanceFCN**                              | https://bit.ly/3wbQuy8 |

| 87   | **FCIS**                                     | https://bit.ly/3dhPz6B |

| 88   | **MaskLab**                                  | https://bit.ly/3wb3Vya |

| 89   | **PANet**                                    | https://bit.ly/2PmQTNs |

| 90   | **CUDMedVision1**                            | https://bit.ly/3rETZd1 |

| 91   | **CUDMedVision2**                            | https://bit.ly/3mago0q |

| 92   | **CFS-FCN**                                  | https://bit.ly/3cXP0zX |

| 93   | **U-net+Res-net**                            | https://bit.ly/3mpKD3P |

| 94   | **Multi-Channel**                            | https://bit.ly/2Q1WCbN |

| 95   | **V-Net**                                    | https://bit.ly/3sYxGAt |

| 96   | **3D-Unet**                                  | https://bit.ly/3uvNOcS |

| 97   | **M²FCN**                                    | https://bit.ly/3cXSlPG |

| 98   | **Suggestive Annotation**                    | https://bit.ly/3t1UbV8 |

| 99   | **3D Unet + Resnet**                         | https://bit.ly/3wRu3i9 |

| 100  | **Cascade 3D-Unet**                          | https://bit.ly/3siNsEX |

| 101  | **DenseVoxNet**                              | https://bit.ly/2RGliYd |

| 102  | **QSA + QNT**                                | https://bit.ly/3wWtyDf |

| 103  | **Attention-Unet**                           | https://bit.ly/3eaMNAK |

| 104  | **RUNet + R2Unet**                           | https://bit.ly/2Q4bIxG |

| 105  | **VoxResNet**                                | https://bit.ly/32gLBWN |

| 106  | **Unet++**                                   | https://bit.ly/3esShGV |

| 107  | **H-DenseUnet**                              | https://bit.ly/3dN53kn |

| 108  | **DUnet**                                    | https://bit.ly/3sPYrWS |

| 109  | **MultiResUnet**                             | https://bit.ly/32J7Epr |

| 110  | **Unet3+**                                   | https://bit.ly/3vj4lRX |

| 111  | **VGGNet For Covid19**                       | https://bit.ly/3ewquW6 |

| 112  | 𝗗𝗲𝗻𝘀𝗲-𝗚𝗮𝘁𝗲𝗱 𝗨-𝗡𝗲𝘁 (𝗗𝗚𝗡𝗲𝘁)                    | https://bit.ly/3tR67cM |

| 113  | **Ki-Unet**                                  | https://bit.ly/3gD4wDK |

| 114  | **Medical Transformer**                      | https://bit.ly/3dLw9Zf |

| 115  | **Deep Snake- Instance Segmentation**        | https://bit.ly/3dQmdhm |

| 116  | **BlendMask**                                | https://bit.ly/32LVXyf |

| 117  | **CenterNet**                                | https://bit.ly/3aJrJQD |

| 118  | **SRCNN**                                    | https://bit.ly/3t82eie |

| 119  | **Swin Transformer**                         | https://bit.ly/2QMWxct |

| 120  | **Polygon-RNN**                              | https://bit.ly/3ujEJ7D |

| 121  | **PolyTransform**                            | https://bit.ly/3gT11ZZ |

| 122  | **D2Det**                                    | https://bit.ly/3b2EDJL |

| 123  | **PolarMask**                                | https://bit.ly/3uklSsO |

| 124  | **FGN**                                      | https://bit.ly/3uiyyAl |

| 125  | **Meta-SR**                                  | https://bit.ly/3ekFyr9 |

| 126  | **Iterative Kernel Correlation**             | https://bit.ly/3xPGZp6 |

| 127  | **SRFBN**                                    | https://bit.ly/2Qc1c7z |

| 128  | **ODE**                                      | https://bit.ly/3w1K8k4 |

| 129  | **SRNTT**                                    | https://bit.ly/2RNT9hS |

| 130  | **Parallax Attention**                       | https://bit.ly/3tIr74x |

| 131  | **3D Super Resolution**                      | https://bit.ly/3bliXJa |

| 132  | **FSTRN**                                    | https://bit.ly/3uWJ8h7 |

| 133  | **PointGroup**                               | https://bit.ly/2QfeKPP |

| 134  | **3D-MPA**                                   | https://bit.ly/3bqz9J6 |

| 135  | **Saliency Propagation**                     |                 https://bit.ly/3tXTvj4 |

| 136  | **Libra R-CNN**                              | https://bit.ly/3hDytnt |

| 137  | **SiamRPN++**                                | https://bit.ly/33TNjyi |

| 138 | **LoFTR** | https://bit.ly/3eUtlJS |

| 139 | **MZSR** | https://bit.ly/3ul5gAs |

| 140 | **UCTGAN** | https://bit.ly/3fQg9ox |

| 141 | **OccuSeg** | https://bit.ly/3bUJtta |

| 142 | **LAPGAN** | https://bit.ly/3unOjW1 |

| 143 | **TPN** | https://bit.ly/3vvyIoW |

| 144 | **GTAD** | https://bit.ly/3c09yqK |

| 145 | **SlowFast** | https://bit.ly/3fMrI0d |

| 146 | **IDU** | https://bit.ly/2ROcIa5 |

| 147 | **ATSS** | https://bit.ly/3hTIflC |

| 148 | **Attention-RPN** | https://bit.ly/3oYescY |

| 149 | **Aug-FPN**                                  | https://bit.ly/3fUbdzi |

| 150 | **Hit-Detector** | https://bit.ly/3uGCLgB |

| 151 | **MCN** | https://bit.ly/3ySpjtq |

| 152 | **CentripetalNet** | https://bit.ly/2S1WNVB |

| 153 | **ROAM** | https://bit.ly/34Ft8Ex |

| 154 | **PF-NET(3D)** | https://bit.ly/2TzQiK9 |

| 155 | **PointAugment** | https://bit.ly/3uMc8Hr |

| 156 | **C-Flow** | https://bit.ly/3xgDlUn |

| 157 | **RandLA-Net** | https://bit.ly/3fYajD9 |

| 158 | **Total3DUnderStanding** | https://bit.ly/3v3jy9c |

| 159 | **IF-Nets** | https://bit.ly/3v7XjPj |

| 160 | **PerfectShape** | https://bit.ly/3za20vk |

| 161 | **ACNe** | https://bit.ly/3gaJQSN |

| 162 | **PQ-Net** | https://bit.ly/35dVPsm |

| 163 | **SG-NN** | https://bit.ly/3iQ4yca |

| 164 | **Cascade Cost Volume** | https://bit.ly/3gyZHtt |

| 165 | **SketchGCN** | https://bit.ly/3pVoxI8 |

| 166 | **Spektral (Graph Neural Network)** | https://bit.ly/3q2T079 |

| 167 | **Graph Convolution Neural Network** | https://bit.ly/3gAkiNX |

| 168 | **Fast Localized Spectral Filtering(Graph Kernel)** | https://bit.ly/3iRUEa0 |

| 169 | **GraphSAGE** | https://bit.ly/3gCj9Xx |

| 170 | **ARMA Convolution** | https://bit.ly/3qcubpC |

| 171 | **Graph Attention Networks** | https://bit.ly/3h1gfKy |

| 172 | **Axial-Deeplab** | https://bit.ly/3qiIF7l |

| 173 | **Tide** | https://bit.ly/3j5evmh |

| 174 | **SipMask** | https://bit.ly/3gMBoJE |

| 175 | **UFO²** | https://bit.ly/2SVS2xA |

| 176 | **SCAN** | https://bit.ly/2ThBv70 |

| 177 | **AABO** : **Adaptive Anchor Box Optimization** | https://bit.ly/3qCSRaP |

| 178 | **SimAug** | https://bit.ly/3dlV6tK |

| 179 | **Instant-teaching** | https://bit.ly/3h0E2LU |

| 180 | **Refinement Network for RGB-D** | https://bit.ly/3dtRh5O |

| 181 | **Polka Lines** | https://bit.ly/3hlNbhd |

| 182 | **HOTR** | https://bit.ly/3hsV44i |

| 183 | **Soft-IntroVAE** | https://bit.ly/3jFozTk |

| 184 | **ReXNet** | https://bit.ly/3r42WO9 |

| 185 | **DiNTS** | https://bit.ly/3AQibii |

| 186 | **Pose2Mesh** | https://bit.ly/3wFTORi |

| 187 | **Keep Eyes on the Lane** | https://bit.ly/3wxs4hl |

| 188 | **AssembleNet++** | https://bit.ly/3xAHhjf |

| 189 | **SNE-RoadSeg** | https://bit.ly/3hyCEAL |

| 190 | **AdvPC** | https://bit.ly/3i3dGrV |

| 191 | **Eagle eye** | https://bit.ly/3e5Iqaz |

| 192 | **Deep Hough Transform** | https://bit.ly/2UEFbAm |

| 193 | **WeightNet** | https://bit.ly/3rfDSUL |

| 194 | **StyleMAPGAN** | https://bit.ly/2URgPTO |

| 195 | **PD-GAN** | https://bit.ly/3xQMCmM |

| 196 | **Non-Local Sparse Attention** | https://bit.ly/3xJZbAd |

| 197 | **TediGAN** | https://bit.ly/3wH67MZ |

| 198 | **FedDG** | https://bit.ly/3zfKiGe |

| 199 | **Auto-Exposure Fusion** | https://bit.ly/3y3F2W1 |

| 200 | **Involution** | https://bit.ly/36Ksiaz |

| 201 | **MutualNet** | https://bit.ly/3zhfd4N |

| 202 | **Teachers do more than teach - Image to Image translation** | https://bit.ly/36RP28K |

| 203 | **VideoMoCo** | https://bit.ly/3f6Pq7Z |

| 204 | **ArtGAN** | https://bit.ly/3rvDCB9 |

| 205 | **Vip-DeepLab** | https://bit.ly/3xmzmVX |

| 206 | **PSConvolution** | https://bit.ly/3rEIgMY |

| 207 | **Deep learning technique on Semantic Segmentation** | https://bit.ly/375hrID |

| 208 | **Synthetic to Real** | https://bit.ly/3yfZSRO |

| 209 | **Panoptic Segmentation** | https://bit.ly/376tbdA |

| 210 | **HistoGAN** | https://bit.ly/3zSYyVD |

| 211 | **Semantic Image Matting** | https://bit.ly/3s5ZD9F |

| 212 | **Anchor-Free Person Search** | https://bit.ly/2VI0KAD |

| 213 | **Spatial-Phase-Shallow-Learning** | https://bit.ly/3CDAl82 |

| 214 | **LiteFlowNet3** | https://bit.ly/3yDILcO |

| 215 | **EfficientNetv2** | https://bit.ly/3xAQsiE |

| 216 | **CBNETv2** | https://bit.ly/3s3ptvb |

| 217 | **PerPixel Classification** | https://bit.ly/3lOomyg |

| 218 | **Kaleido-BERT** | https://bit.ly/3ywh2Lf |

| 219 | **DARKGAN** | https://bit.ly/3lTW05J |

| 220 | **PPDM** | https://bit.ly/3lPgjBt |

| 221 | **SEAN** | https://bit.ly/3yOUJ3L |

| 222 | **Closed-Loop Matters** | https://bit.ly/3CzBnlq |

| 223 | **Elastic Graph Neural Network** | https://bit.ly/3jket9S |

| 224 | **Deep Imbalance Regression** | https://bit.ly/3yn0Ue3 |

| 225 | **PIPAL** - Image Quality Assessment | https://bit.ly/3gCliSx |

| 226 | **Mobile-Former** | https://bit.ly/3kxCSbm |

| 227 | **Rank and Sort Loss** | https://bit.ly/3sPQt1s |

| 228 | **Room Classification using Graph Neural Network** | https://bit.ly/3gD8Odv |

| 229 | **Pyramid Vision Transformer** | https://bit.ly/3zmod9h |

| 230 | **EigenGAN** | https://bit.ly/3BfdIVO |

| 231 | **GNeRF** | https://bit.ly/3mD3kTR |

| 232 | **DetCo** | https://bit.ly/3sQiRk9 |

| 233 | **DERT with Special Modulated Co-Attention**                 | https://bit.ly/3sPQ5jw |

|      | **Residual Attention** | https://bit.ly/3yni4bJ |

| 235 | **MG-GAN** | https://bit.ly/3mD30o7 |

| 236 | **Adaptable GAN Encoders** | https://bit.ly/3yh4XJ3 |

| 237 | **AdaAttN** | https://bit.ly/3BepKPa |

| 238 | **Conformer** | https://bit.ly/3gCkj4N |

| 239 | **YOLOP** | https://bit.ly/3BicysB |

| 240 | **VMNet** | https://bit.ly/3k73jFZ |

| 241 | **Airbert** | https://bit.ly/3nvcrGs |

| 242 | 𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗥-𝗖𝗡𝗡 | https://bit.ly/397Zius |

| 243 | **Battle of Network Structure** | https://bit.ly/2XcHbB0 |

| 244 | **InSeGAN** | https://bit.ly/3z9wyMF |

| 245 | **Efficient Person Search** | https://bit.ly/3CpbZOr |

| 246 | **DeepGCNs** | https://bit.ly/3AevSHg |

| 247 | **GroupFormer** | https://bit.ly/3lqzm2Y |

| 248 | **SLIDE** | https://bit.ly/3hwpiEp |

| 249 | **Super Neuron** | https://bit.ly/3zkXE3D |

| 250 | **SOTR** | https://bit.ly/3hvqCYl |

| 251 | **Survey : Instance Segmentation** | https://bit.ly/3k90xQB |

| 252 | **SO-Pose** | https://bit.ly/3C56KD8 |

| 253 | **CANet** | https://bit.ly/2XlDKZ2 |

| 254 | **XVFI** | https://bit.ly/3lrOpcZ |

| 255 | **TxT** | https://bit.ly/3tGFlEH |

| 256 | **ConvMLP** | https://bit.ly/2XlE8Xu |

| 257 | **Cross Domain Contrastive Learning** | https://bit.ly/3tDb2id |

| 258 | **OS2D: One Stage Object Detection** | https://bit.ly/3ufnEMD |

| 259 | **PointManifoldCut** | https://bit.ly/3CKvAIL |

| 260 | **Large Scale Facial Expression Dataset** | https://bit.ly/2ZqtT4V |

| 261 | **Graph-FPN** | https://bit.ly/2XH8T9f |

| 262 | **3D Shape Reconstruction** | https://bit.ly/2XTe9aq |

| 263 | **Open Graph Benchmark Dataset** | https://bit.ly/3ET2Lfl |

| 264 | **ShiftAddNet** | https://bit.ly/3i6eb5C |

| 265 | **WatchOut! Motion Blurring the vision of your DNN** | https://bit.ly/3CKTzrw |

| 266 | **Rethinking Learnable Tree Filter** | https://bit.ly/3zHfPAC |

| 267 | **Neuron Merging** | https://bit.ly/39DwLNS |

| 268 | **Distance IOU Loss** | https://bit.ly/3i7Zj6z |

| 269 | **Deep Imitation learning** | https://bit.ly/3AzGVd6 |

| 270 | **Pixel Level Cycle Association** | https://bit.ly/3iTZMK6 |

| 271 | **Deep Model Fusion** | https://bit.ly/2YK45kl |

| 272 | **Object Representation Network** | https://bit.ly/3BA0mnE |

| 273 | **HOI Analysis** | https://bit.ly/3FH2Key |

| 274 | **Deep Equilibrium Models** | https://bit.ly/3FDH2IB |

| 275 | **Sampling from k-DPP** | https://bit.ly/3BAyRuc |

| 276 | **Rotated Binary Neural Network** | https://bit.ly/3mIuYx3 |

| 277 | **PP-LCNet** - **LightCNN** | https://bit.ly/3v1Zh5H |

| 278 | **MC-Net+** | https://bit.ly/3v5tYqk |

| 279 | **Fake it till you make it** | https://bit.ly/3AyGTSQ |

| 280 | **Enformer** | https://bit.ly/3AAdCr9 |

| 281 | **VideoClip** | https://bit.ly/3mOueGu |

| 282 | **Moving Fashion** | https://bit.ly/3jdvAtN |

| 283 | **Convolution to Transformer** | https://bit.ly/3v5yy8f |

| 284 | **HeadGAN** | https://bit.ly/3BLzRvm |

| 285 | **Focal Transformer** | https://bit.ly/3lvCYSI |

| 286 | **StyleGAN3** | https://bit.ly/3kvFPKw |

| 287 | **3Detr:3D Object Detection** | https://bit.ly/3Hfk6A8 |

| 288 | **Do Self-Supervised and Supervised Methods Learn Similar Visual Representations?** | https://bit.ly/3kyWM6H |

| 289 | **Back to the Features** | https://bit.ly/3kvsxh3 |

| 290 | **Anticipative Video Transformer** | https://bit.ly/30mADl2 |

| 291 | **Attention Meets Geometry** | https://bit.ly/3kweSpZ |

| 292 | **DeepMoCaP:** Deep Optical Motion Capture | https://bit.ly/30mjTdT |

| 293 | **TrOCR: Transformer-based Optical Character Recognition** | https://bit.ly/3DqenW5 |

| 294 | **Moving Fashion** | https://bit.ly/2YGtjA1 |

| 295 | **StyleNeRF** | https://bit.ly/31W4Mbz |

| 296 | **ECA-Net: :Efficient Channel Attention** | https://bit.ly/3n92i1s |

| 297 | **Inferring High Resolution Traffic Accident risk maps** | https://bit.ly/3HgovD6 |

| 298 | **Bias Loss: For Mobile Neural Network** | https://bit.ly/3qvBPNO |

| 299 | **ByteTrack: Multi-Object Tracking** | https://bit.ly/3c3l7wQ |

| 300 | **Non-Deep Network** | https://bit.ly/3qwZwoV |

| 301 | **Temporal Attentive Covariance** | https://bit.ly/3ontCbP |

| 302 | **Plan-then-generate: Controlled Data to Text Generation** | https://bit.ly/3DcbsA6 |

| 303 | **Dynamic Visual Reasoning** | https://bit.ly/31Q4BhP |

| 304 | **MedMNIST: Medical MNIST Dataset** | https://bit.ly/3qxuqxq |

| 305 | **Colossal-AI: A PyTorch-Based Deep Learning System For Large-Scale Parallel Training** | https://bit.ly/3wG6Xv8 |

| 306 | **Recursively Embedded Atom Neural Network(REANN)** | https://bit.ly/3F1JKqe |

| 307 | **PolyTrack: for fast multi-object tracking and segmentation** | https://bit.ly/3DeBmmS |

| 308 | **Can contrastive learning avoid shortcut solutions?** | https://bit.ly/3wHJIk9 |

| 309 | **ProjectedGAN:  To Improve Image Quality** | https://bit.ly/30hw8Zm |

| 310 | **Arch-Net:  A Family Of Neural Networks Built With Operators To Bridge The Gap ** | https://bit.ly/3oFOCef |

| 311 | **PP-ShiTu:A Practical Lightweight Image Recognition System** | https://bit.ly/3naurFw |

| 312 | **EditGAN** | https://bit.ly/30gYd2Z |

| 313 | **Panoptic 3D Scene Segmentation** | https://bit.ly/3caSvla |

| 314 | **PARP: Improve the Efficiency of NN** | https://bit.ly/3DakTjt |

| 315 | **WORD: Organ Segmentation Dataset** | https://bit.ly/3qv5OW2 |

| 316 | **DenseULearn** | https://bit.ly/3ohRiyi |

| 317 | **Does Thermal data make the detection systems more reliable?** | https://bit.ly/3sQgTSO |

| 318 | **MADDNESS: Approximate Matrix Multiplication (AMM)** | https://bit.ly/3zgVIL4 |

| 319 | **Deceive D: Adaptive Pseudo Augmentation** | https://bit.ly/3sIG6yA |

| 320 | **OadTR** | https://bit.ly/3JsUHUF |

| 321 | **OnePassImageNet** | https://bit.ly/3sKL6Ti |

| 322 | **Image-specific Convolutional Kernel Modulation for Single Image Super-resolution** | https://bit.ly/3FUpA20 |

| 323 | **TransMix** | https://bit.ly/3EH93gH |

| 324 | **PytorchVideo** | https://bit.ly/3JvgDP7 |

| 325 | **MetNet-2** | https://bit.ly/3sMZb2M |

| 326 | **Unsupervised deep learning identifies semantic disentanglement** | https://bit.ly/3JyAwVi |

| 327 | **Story Visualization** | https://bit.ly/3qB554i |

| 328 | **MetaFormer** | https://bit.ly/3sLBebP |

| 329 | **GauGAN2** | https://bit.ly/3pGrIVH |

| 330 | **SciGAP** | https://bit.ly/3EB7e4U |

| 331 | **Generative Flow Networks (GFlowNets)** | https://bit.ly/3Jv9YEz |

| 332 | **Ensemble Inversion** | https://bit.ly/3ECwbg9 |

| 333 | **SAVi** | https://bit.ly/3eF6txe |

| 334 | **Digital Optical Neural Network** | https://bit.ly/3EI07rh |

| 335 | **Image-Generation Research With Manifold Matching Via Metric Learning** | https://bit.ly/3FUomnq |

| 336 | **GHN-2(Graph HyperNetworks)** | https://bit.ly/3qzc5yB |

| 337 | **NeatNet** | https://bit.ly/3sLY17r |

| 338 | **NeuralProphet** | https://bit.ly/3JrUK38 |

| 339 | **Background Activation Suppression for Weakly Supervised Object Detection** | https://bit.ly/3Jvyzt2 |

| 340 | **Learning to Detect Every Thing in an Open World** | https://bit.ly/3mKxOTc |

| 341 | **PoolFormer** | https://bit.ly/3qFHNtS |

| 342 | **GLIP** | https://bit.ly/3mK3bgx |

| 343 | **PHALP** | https://bit.ly/3eJJvEV |

| 344 | **PixMix** | https://bit.ly/3Hqh77m |

| 345 | **CodeNet** | https://bit.ly/32RPx3X |

| 346 | **GANgealing** | https://bit.ly/3EIkO6k |

| 347 | **Semantic Diffusion Guidance** | https://bit.ly/3JsNzI3 |

| 348 | **TokenLearner** | https://bit.ly/3mLG4lM |

| 349 | **Temporal Fusion Transformer (TFT)** | https://bit.ly/3JuHcno |

| 350 | **HiClass: Evaluation Metrics for Local Hierarchical Classification** | https://bit.ly/3JHmn8H |

| 351 | **Stable Long Term Recurrent Video Super Resolution** | https://bit.ly/3qFlPHl |

| 352 | **AdaViT** | https://bit.ly/3eDASMj |

| 353 | **Few-Shot Learner (FSL)** | https://bit.ly/3ELOOym |

| 354 | **Exemplar Transformers** | https://bit.ly/3qzJE3C |

| 355 | **StyleSwin** | https://bit.ly/3HqkCe4 |

| 356 | **RepMLNet** | https://bit.ly/32DxbUu |

| 357 | **2 Stage Unet** | https://bit.ly/3JGjIMq |

| 358 | **Untrained Deep NN** | https://bit.ly/3JplL7r |

| 359 | **SeMask** | https://bit.ly/3zfouM8 |

| 360 | **JoJoGAN** | https://bit.ly/31gl9Qi |

| 361 | **ELSA** | https://bit.ly/3mLWScb |

| 362 | **PRIME** | https://bit.ly/3FI14RZ |

| 363 | **GLIDE** | https://bit.ly/31ixB20 |

| 364 | **StyleGAN-V** | https://bit.ly/3Jvx91G |

| 365 | **SLIP: Self-supervision meets Language-Image Pre-training** | https://bit.ly/3qAjL3r |

| 366 | **SmoothNet: A Plug-and-Play Network for Refining Human Poses in Videos** | https://bit.ly/3tYNxlp |

| 367 | **Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results** | https://bit.ly/3tZFyEQ |

| 368 | **PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability** | https://bit.ly/3LCKENk |

| 369 | **Vision Transformer with Deformable Attention** | https://bit.ly/3tY3s3k |

| 370 | **A Transformer-Based Siamese Network for Change Detection** | https://bit.ly/3DxPYP5 |

| 371 | **Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention** | https://bit.ly/3qRsTle |

| 372 | **SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection** | https://bit.ly/3tXduls |

| 373 | **HyperionSolarNet: Solar Panel Detection from Aerial Images** | https://bit.ly/35v2rX6 |

| 374 | **Realistic Full-Body Anonymization with Surface-Guided GANs** | https://bit.ly/3DwBNd4 |

| 375 | **Generalized Category Discovery** | https://bit.ly/3IZ1HaC |

| 376 | **KerGNNs: Interpretable Graph Neural Networks with Graph Kernels** | https://bit.ly/3DtWtlU |

| 377 | **Optimization Planning for 3D ConvNets** | https://bit.ly/3K38e5p |

| 378 | **gDNA: Towards Generative Detailed Neural Avatars** | https://bit.ly/3DEtFHC |

| 379 | **SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps** | https://bit.ly/3NIieTA |

| 380 | **HYDLA: Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning** | https://bit.ly/379dy8v |

| 381 | **HardBoost: Boosting Zero-Shot Learning with Hard Classes** | https://bit.ly/379diX5 |

| 382 | **DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images** | https://bit.ly/3Lu0UzU |

| 383 | **Q-ViT: Fully Differentiable Quantization for Vision Transformer** | https://bit.ly/3qXv9Ym |

| 384 | **SPAMs: Structured Implicit Parametric Models** | https://bit.ly/3iU95cL |

| 385 | **GeoFill: Reference-Based Image Inpainting of Scenes with Complex Geometry** | https://bit.ly/3qUwCP6 |

| 386 | **Improving language models by retrieving from trillions of tokens** | https://bit.ly/37aKsG5 |

| 387 | **StylEx finds and visualizes disentangled attributes that affect a classifier automatically.** | https://bit.ly/3qYwYEf |

| 388 | **‘ReLICv2’: Pushing The Limits of Self-Supervised ResNet** | https://bit.ly/3JZXy7C |

| 389 | **‘Detic’: A Method to Detect Twenty-Thousand Classes using Image-Level Supervision** | https://bit.ly/3iRtsqZ |

| 390 | **Momentum Capsule Networks** | https://bit.ly/3NFDv0j |

| 391 | **RelTR: Relation Transformer for Scene Graph Generation** | https://bit.ly/3iVBWgB |

| 392 | **Transformer based SAR Images Despecking** | https://bit.ly/3qWeILH |

| 393 | **ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation** | https://bit.ly/3wWGY4T |

| 394 | **VRT: A Video Restoration Transformer** | https://bit.ly/3K44YXw |

| 395 | **You Only Cut Once: Boosting Data Augmentation with a Single Cut** | https://bit.ly/36L8pDW |

| 396 | **StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets** | https://bit.ly/3iRlEp8 |

| 397 | **The KFIoU Loss for Rotated Object Detection** | https://bit.ly/3NHUL5e |

| 398 | **The Met Dataset: Instance Level Recognition** | https://bit.ly/3K7lPJ2 |

| 399 | **Alphacode: a System that can  compete at average human level** | https://bit.ly/3qXIIH5 |

| 400 | **Third Time's the Charm? Image and Video Editing with StyleGAN3** | https://bit.ly/35vAoqx |

| 401 | **NeuralFusion: Online Depth Fusion in Latent Space** | https://bit.ly/3uFaysA |

| 402 | **VOS: Learning what you don't know by VIRTUAL OUTLIER SYNTHESIS** | https://bit.ly/3uPG9rG |

| 403 | **Self-Conditioned Generative Adversarial Networks for Image Editing** | https://bit.ly/3tX8m0u |

| 404 | **TransformNet: Self-supervised representation learning through predicting geometric transformations** | https://bit.ly/3uOCfPM |

| 405 | **YOLOv7 - Framework Beyond Detection** | https://bit.ly/3wXU81y |

| 406 | **F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization** | https://bit.ly/3DzhFXU |

| 407 | **Block-NeRF: Scalable Large Scene Neural View Synthesis** | https://bit.ly/3LyELk5 |

| 408 | **Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition** | https://bit.ly/375C76y |

| 409 | **COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasets** | https://bit.ly/3NCK6bZ |

| 410 | **ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification** | https://bit.ly/3uJuMBz |

| 411 | **Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges** | https://bit.ly/388imeT |

| 412 | **How Do Vision Transformers Work?** | https://bit.ly/3NE1mO2 |

| 413 | **Mirror-Yolo: An attention-based instance segmentation and detection model for mirrors** | https://bit.ly/3LBS96P |

| 414 | **PENCIL: Deep Learning with Noisy Labels** | https://bit.ly/3iXvHc4 |

| 415 | **VLP: A Survey on Vision-Language Pre-training** | https://bit.ly/3J0v2RZ |

| 416 | **Visual Attention Network** | https://bit.ly/3Dt7rbv |

| 417 | **GroupViT: Semantic Segmentation Emerges from Text Supervision** | https://bit.ly/3NQv7eG |

| 418 | **Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis** | https://bit.ly/373xs4T |

| 419 | **End to End Cascaded Image De-raining and Object Detetion NN** | https://bit.ly/375PLGw |

| 420 | **Level-K to Nash Equilibrium** | https://bit.ly/3NFRX8t |

| 421 | **Machine Learning for Mechanical Ventilation Control** | https://bit.ly/3JZCMEV |

| 422 | **The effect of fatigue on the performance of online writer recognition** | https://bit.ly/3wXSSLS |

| 423 | **State-of-the-Art in the Architecture, Methods and Applications of StyleGAN** | https://bit.ly/3iRjl5s |

| 424 | **Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation** | https://bit.ly/3v5XZXR |

| 425 | **Self-supervised Transformer for Deepfake Detection** | https://bit.ly/3tXtUdk |

| 426 | **CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size** | https://bit.ly/3LxkrQa |

| 427 | **TCTrack: Temporal Contexts for Aerial Tracking** | https://bit.ly/3uM5O4B |

| 428 | **LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction** | https://bit.ly/3uOfKe0 |

| 429 | **HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening** | https://bit.ly/35tRV2j |

| 430 | **ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization** | https://bit.ly/3LwoMmy |

| 431 | **MLSeg: Image and Video Segmentation** | https://bit.ly/38p9iCN |

| 432 | **Image Steganography based on Style Transfer** | https://bit.ly/3DJHLaN |

| 433 | **GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains** | https://bit.ly/3JYPrIg |

| 434 | **AGCN: Augmented Graph Convolutional Network** | https://bit.ly/3DwZrWN |

| 435 | **StyleBabel: Artistic Style Tagging and Captioning** | https://bit.ly/3j1Klit |

| 436 | **ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI** | https://bit.ly/38maN4z |

| 437 | **InsetGAN for Full-Body Image Generation** | https://bit.ly/3Dsu9At |

| 438 | **Implicit Feature Decoupling with Depthwise Quantization** | https://bit.ly/3K1mxaA |

| 439 | **Bamboo: Building Mega-Scale Vision Dataset** | https://bit.ly/3wVPalD |

| 440 | **TensoRF: Tensorial Radiance Fields** | https://bit.ly/3iWAFWI |

| 441 | **FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition** | https://bit.ly/3NCHTxd |

| 442 | **One-Shot Adaptation of GAN in Just One CLIP** | https://bit.ly/36NOPab |

| 443 | **SHREC 2021: Classification in cryo-electron tomograms** | https://bit.ly/3iSXpqv |

| 444 | **MaskGIT: Masked Generative Image Transformer** | https://bit.ly/3qSQz8I |

| 445 | **Detection, Recognition, and Tracking: A Survey** | https://bit.ly/378G8qw |

| 446 | **Mixed Differential Privacy** | https://bit.ly/3IZ0MGU |

| 447 | **Mixed DualStyleGAN** | https://bit.ly/3wTyAmD |

| 448 | **BigDetection** | https://bit.ly/3DuZSRk |

| 449 | **Feature visualization for convolutional neural network** | https://bit.ly/3Dwf6FJ |

| 450 | **AutoAvatar** | https://bit.ly/38m9ClF |

| 451 | **A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction** | https://bit.ly/3Dz1idF |

| 452 | **StyleT2I** | https://bit.ly/35u5Wx0 |

| 453 | **L^3U-net** | https://bit.ly/3iTOq8r |

| 454 | **Balanced MSE** | https://bit.ly/3rxt7yo |

| 455 | **BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers** | https://bit.ly/36m3HfC |

| 456 | **TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing** | https://bit.ly/3JQKZKS |

| 457 | **On the Importance of Asymmetry for Siamese Representation Learning** | https://bit.ly/3JNgcyt |

| 458 | **On One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks** | https://bit.ly/3uQTC3P |

| 459 | **Pyramid Frequency Network with Spatial Attention Residual Refinement Module for Monocular Depth** | https://bit.ly/3KWT6a4 |

| 460 | **Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection** | https://bit.ly/3L8a59H |

| 461 | **DaViT: Dual Attention Vision Transformers** | https://bit.ly/3Engc7e |

| 462 | **SPAct: Self-supervised Privacy Preservation for Action Recognition** | https://bit.ly/3KTNvRW |

| 463 | **Class-Incremental Learning with Strong Pre-trained Models** | https://bit.ly/3MdlcOq |

| 464 | **RBGNet: Ray-based Grouping for 3D Object Detection by Center for Data Science** | https://bit.ly/3EqkydH |

| 465 | **Event Transformer** | https://bit.ly/3KUsMxc |

| 466 | **ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension** | https://bit.ly/3M6RgDE |

| 467 | **A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research** | https://bit.ly/3xAyqRj |

| 468 | **Simple Baselines for Image Restoration** | https://bit.ly/3vt4tjB |

| 469 | **Masked Siamese Networks for Label-Efficient Learning** | https://bit.ly/3viEs6s |

| 470 | **Neighborhood Attention Transformer** | https://bit.ly/3jNExK3 |

| 471 | **TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation** | https://bit.ly/3M3EA0K |

| 472 | **MVSTER: Epipolar Transformer for Efficient Multi-View Stereo** | https://bit.ly/3MaDTCR |

| 473 | **Temporally Efficient Vision Transformer for Video Instance Segmentation** | https://bit.ly/3w6xkf3 |

| 474 | **EditGAN: High-Precision Semantic Image Editing** | https://bit.ly/3yx2JJ2 |

| 475 | **CenterNet++ for Object Detection** | https://bit.ly/3woxrBG |

| 476 | **A case for using rotation invariant features in state of the art feature matchers** | https://bit.ly/3kZ1x9A |

| 477 | **WebFace260M: A Benchmark for Million-Scale Deep Face Recognition** | https://bit.ly/3w2T3Vd |

| 478 | **JIFF: Jointly-aligned Implicit Face Function for High-Quality Single View Clothed Human Reconstruction** | https://bit.ly/3N9Me9U |

| 479 | **Image Data Augmentation for Deep Learning: A Survey** | https://bit.ly/3PfC1uA |

| 480 | **StyleGAN-Human: A Data-Centric Odyssey of Human Generation** | https://bit.ly/3PqV710 |

| 481 | **Few-shot Head Swapping In The Wild Secrets Revealed By Department Of Computer Vision Technology (vis)** | https://bit.ly/3w7xm6c |

| 482 | **CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP** | https://bit.ly/3N3cEKu |

| 483 | **HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling** | https://bit.ly/3Nqnevx |

| 484 | **Generative Adversarial Networks for Image Super-Resolution: A Survey** | https://bit.ly/39jyL0U |

| 485 | **CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification** | https://bit.ly/3N7Qd6V |

| 486 | **C3-STISR: Scene Text Image Super-resolution with Triple Clues** | https://bit.ly/3l1352C |

| 487 | **Barbershop: GAN-based Image Compositing using Segmentation Masks** | https://bit.ly/39hus6d |

| 488 | **DANBO: Disentangled Articulated Neural Body Representations** | https://bit.ly/3LkqWp3 |

| 489 | **BlobGAN: Spatially Disentangled Scene Representations** | https://bit.ly/3sufEYz |

| 490 | **Text to artistic image generation** | https://bit.ly/3w6wzmd |

| 491 | **Sequencer: Deep LSTM for Image Classification** | https://bit.ly/3sulPvT |

| 492 | **IVY: An Open-Source Tool To Make Deep Learning Code Compatible Across Frameworks** | https://bit.ly/3M6MbvJ |

| 493 | **Introspective Deep Metric Learning** | https://bit.ly/3w2pZ02 |

| 494 | **KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints** | https://bit.ly/3wnRhwF |

| 495 | **GraphWorld: A Methodology For Analyzing The Performance Of GNN Architectures On Millions Of Synthetic Benchmark Datasets** | https://bit.ly/3PUQexk |

| 496 | **Group R-CNN for Weakly Semi-supervised Object Detection with Points** | https://bit.ly/3zfvU3W |

| 497 | **Few-Shot Head Swapping in the Wild** | https://bit.ly/3xapGkn |

| 498 | **StyLandGAN: A StyleGAN based Landscape Image Synthesis using Depth-map** | https://bit.ly/3GKX4Bi |

| 499 | **Spiking Approximations of the MaxPooling Operation in Deep SNNs** | https://bit.ly/3GLp7AG |

| **500** | **Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization** | https://bit.ly/3NTGsJQ |

***Thanks for Reading🎉🎉🎉🎉***

----