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 |
|
|
318 |
|
|
319 |
|
|
320 |
|
|