There are the overall of papers about Deep Learning.
https://github.com/Gojay001/DeepLearning-pwcn
Contents
- Classification
- LeNet-5
- AlexNet
- NIN(Network In Network)
- VGG
- GoogLeNet(Inception-v1)
- ResNet
- Inception-v4
- DenseNet
- DLA(Deep Layer Aggregation)
- ShuffleNet
- MobileNetV3
- Detection
- One-stage
- SSD
- YOLO
- YOLOv2
- RetinaNet
- YOLOv3
- CornerNet
- CenterNet
- YOLOv4
- YOLOF
- Two-stage
- R-CNN
- SPP
- Fast R-CNN
- Faster R-CNN
- FPN
- One-stage
- Segmentation
- FCN
- U-Net
- Seg-Net
- DeepLab V1
- PSPNet
- DeepLab V2
- Mask R-CNN
- DeepLab V3
- PointNet
- PointNet++
- DeepLab V3+
- DGCNet(Dual GCN)
- SETR(SEgmentation TRansfomer)
- Segmenter
- SegFormer
- FTN(Fully Transformer Networks)
- Tracking
- MOT
- SORT
- DeepSORT
- Tracktor
- FFT(Flow-Fuse Tracker)
- JRMOT
- Tracklet
- DMCT(Deep Multi-Camera Tracking)
- FairMOT
- CenterPoint
- VOT
- DepthTrack
- BinocularTrack
- SiamFC
- SiamRPN
- SiamRPN++
- SiamMask
- GlobalTrack
- PAMCC-AOT
- SiamCAR
- SiamBAN
- SiamAttn
- TSDM
- SiamGAT
- RE-SiamNets
- MOT
- FSS
- OSLSM
- co-FCN
- AMP(Adaptive Masked Proxies)
- SG-One(Similarity Guidance)
- CENet(Combinatorial Embedding Network)
- PANet(Prototype Alignment)
- CANet(Class Agnostic)
- PGNet(Pyramid Graph Network)
- CRNet(Cross-Reference Network)
- FGN(Fully Guided Network)
- OTB(On the Texture Bias)
- LTM(Local Transformation Module)
- SimPropNet(Similarity Propagation)
- PPNet(Part-aware Prototype)
- PFENet(Prior Guided Feature Enrichment Network)
- PMMs(Prototype Mixture Models)
- GFS-Seg(Generalized Few-Shot)
- SCL(Self-Corss Learning)
- ASGNet(Adaptive Superpixel-guided Network)
- HSNet(Hypercorrelation Squeeze)
- BAM
- 3D-Face
- 3DMM
- CameraCalibration
- Bilinear
- DDE
- FaceWarehouse
- Face2Face
- DynamicAvatars
- FLAME
- Nonlinear
- DynamicRigidityPrior
- Deep3D
- SimpleAnimation
- RingNet
- FOCUS
- MICA
- HRN
- Attention
- Transformer
- Non-local
- Image Transformer
- ViT(Vision Transformer)
- Swin Transformer
- ResT
- DS-Net(Dual Stream Network)
- TransCNN
- Shuffle Transformer
- RGBD-SOT
- UC-Net
- JL-DCF(Joint Learning and Densely-Cooperative Fusion)
- SA-Gate(Separation-and-Aggregation Gate)
- BiANet(Bilateral Attention Network)
- DSA^2F(Depth-Sensitive Attention and Automatic Multi-Modal Fusion)
- Unsupervised
- SimSiam
- Detection-3D
- PV-RCNN
- FSL
- RN(Relation Network)
- GAN
- GAN
- BeautyGAN
- Optimization
- ReLU
- Momentum
- Dropout
- Adam
- BN
- GDoptimization
- Survey
- 3D-Detection-Survey-2019
- FSL-Survey-2019
- MOT-Survey-2020
- Transformer-Survey-2021
Image Classification
Title | Paper | Conf | Code |
---|---|---|---|
LeNet-5 | Gradient-based learning applied to document recognition | IEEE(1998) | [code] |
AlexNet | ImageNet Classification with Deep Convolutional Neural Networks | NIPS(2012) | [code] |
NIN | Network In Network | arXiv(2013) | PyTorch |
VGG | Very Deep Convolutional Networks for Large-Scale Image Recognition | ICLR(2015) | [code] |
GoogLeNet | Going deeper with convolutions | CVPR(2015) | PyTorch |
ResNet | Deep Residual Learning for Image Recognition | CVPR(2016) | PyTorch |
Inception-v4 | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning | AAAI(2017) | [code] |
DenseNet | Densely Connected Convolutional Networks | CVPR(2017) | PyTorch |
DLA | Deep Layer Aggregation | CVPR(2018) | PyTorch |
ShuffleNet | ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices | CVPR(2018) | [code] |
MobileNetV3 | Searching for MobileNetV3 | ICCV(2019) | [code] |
More information can be found in Awesome - Image Classification.
Object Detection
More information can be found in awesome-object-detection.
Object Segmentation
Object Tracking
Few-Shot Segmentation
More information can be found in Few-Shot-Semantic-Segmentation-Papers.
3D Face Reconstruction and Facial Animation
Attention or Transformer
Title | Paper | Conf | Code |
---|---|---|---|
Transformer | Attention Is All You Need | arXiv(2017) | TensorFlow |
Non-local | Non-local Neural Networks | CVPR(2018) | PyTorch |
Image Transformer | Image Transformer | arXiv(2018) | [code] |
ViT | An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale | arXiv(2020) | PyTorch |
Swin Transformer | Swin Transformer: Hierarchical Vision Transformer using Shifted Windows | arXiv(2021) | PyTorch |
ResT | ResT: An Efficient Transformer for Visual Recognition | arXiv(2021) | PyTorch |
DS-Net | Dual-stream Network for Visual Recognition | arXiv(2021) | [code] |
TransCNN | Transformer in Convolutional Neural Networks | arXiv(2021) | PyTorch |
Shuffle Transformer | Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer | arXiv(2021) | PyTorch |
Salient Object Detection
Title | Paper | Conf | Code |
---|---|---|---|
UC-Net | UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders | CVPR(2020) | PyTorch |
JL-DCF | JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection | CVPR(2020) | PyTorch |
SA-Gate | Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation | ECCV(2020) | PyTorch |
BiANet | Bilateral Attention Network for RGB-D Salient Object Detection | TIP(2021) | [Code] |
DSA^2F | Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion | CVPR(2021) | [Code] |
Unsupervised Learning
Title | Paper | Conf | Code |
---|---|---|---|
SimSiam | Exploring Simple Siamese Representation Learning | CVPR(2021) | PyTorch |
3D Object Detection
Title | Paper | Conf | Code |
---|---|---|---|
PV-RCNN | PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection | CVPR(2020) | PyTorch |
Few-Shot Learning
Title | Paper | Conf | Code |
---|---|---|---|
RN | Learning to Compare: Relation Network for Few-Shot Learning | CVPR(2018) | PyTorch |
Generative Adversarial Network
Title | Paper | Conf | Code |
---|---|---|---|
GAN | Generative Adversarial Networks | arXiv(2014) | [code] |
BeautyGAN | BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network | ACM MM(2018) | TensorFlow |
Optimization
Title | Paper | Conf | Code |
---|---|---|---|
ReLU | Deep Sparse Rectifier Neural Networks | JMLR(2011) | [code] |
Momentum | On the importance of initialization and momentum in deep learning | ICML(2013) | [code] |
Dropout | Dropout: a simple way to prevent neural networks from overfitting | JMLR(2014) | [code] |
Adam | Adam: A Method for Stochastic Optimization | ICLR(2015) | [code] |
BN | Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift | ICML(2015) | [code] |
GDoptimization | An overview of gradient descent optimization algorithms | arXiv(2016) | [code] |
Survey
Title | Paper | Conf |
---|---|---|
3D-Detection-Survey-2019 | A Survey on 3D Object Detection Methods for Autonomous Driving Applications | ITS(2019) |
FSL-Survey-2019 | Generalizing from a Few Examples: A Survey on Few-Shot Learning | CSUR(2019) |
MOT-Survey-2020 | Deep Learning in Video Multi-Object Tracking: A Survey | Neurocomputing(2020) |
Transformer-Survey-2021 | A Survey of Transformers | arXiv(2021) |