365-Days-Computer-Vision-Learning-Linkedin-Post
365-Days-Computer-Vision-Learning-Linkedin-Post copied to clipboard
Published
20 hours ago โข
ashishpatel26
ashishpatel26
365 Days Computer Vision Learning Linkedin Post
365 Days Computer Vision Learning LinkedIn Post
Follow me on LinkedIn : https://www.linkedin.com/in/ashishpatel2604/
| 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 | BRยฒNet | 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 | MยฒFCN | 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 | ๐๐ฒ๐ป๐๐ฒ-๐๐ฎ๐๐ฒ๐ฑ ๐จ-๐ก๐ฒ๐ (๐๐๐ก๐ฒ๐) | 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 | UFOยฒ | 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 | ๐ข๐ฟ๐ถ๐ฒ๐ป๐๐ฒ๐ฑ ๐ฅ-๐๐ก๐ก | 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 | โReLICv2โ: Pushing The Limits of Self-Supervised ResNet | https://bit.ly/3JZXy7C |
| 389 | โDeticโ: 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 Reading๐๐๐๐
Metadata
504
Stars
155
Forks
Watchers
Owner
ashishpatel26
Metadata
365 Days Computer Vision Learning Linkedin Post