| 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 |
|
|
| 287 |
|
|
| 288 |
|
|
| 289 |
|
|
| 290 |
|
|
| 291 |
|
|
| 292 |
|
|
| 293 |
|
|
| 294 |
|
|
| 295 |
|
|
| 296 |
|
|
| 297 |
|
|
| 299 |
|
|
| 300 |
|
|
