Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/WonJunPark/Efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/WoominM/DeGCN_pytorch
https://github.com/Wuxiao85/paddle_EfficientGCNv
Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
https://github.com/Xiehuaiqi/3D-ResNets-PyTorch-master
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/XinyuSun/M3Video
Masked Motion Encoding for Self-Supervised Video Representation Learning
https://github.com/XinzeWu/st-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/XuHao9166/TSN_BehaviorRecognition_ForwardCalculation
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/XuHao9166/TSN_BehaviorRecognition_GenerateTrainImages
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/YanWei123/PointNet-encoder-and-FoldingNet-decoder-add-quantization-change-latent-code-size-from-512-to-1024
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YanWei123/Pointnet_encoder_Foldingnet_decoder_quantization
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YanWei123/Pytorch-implementation-of-FoldingNet-encoder-and-decoder-with-graph-pooling-covariance-add-quanti
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Yang2446/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YashNita/Separate-Object-Sounds-by-Watching-Unlabeled-Video-PyTorch-
Im2Flow: Motion Hallucination from Static Images for Action Recognition
https://github.com/YigitGunduc/Conditional-GANs-CGANs
https://github.com/Yindong-Zhang/myGAT
https://github.com/YiruS/pointnet_adversarial
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Yonder-OSS/D3M-Primitives
https://github.com/Young98CN/pointconv_pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Ysp9714/SlowFastNet-keras
https://github.com/YunseobShin/wiki_GAT
https://github.com/YutingLi0606/SURE
https://github.com/Yuto0107/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/YvanG/VQGAN-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ZJCV/Non-local
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/ZJCV/TSN
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/ZJUT-ERCISS/c3d_mindspore
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/i3d.py
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/r2plus1d.py
A Closer Look at Spatiotemporal Convolutions for Action Recognition
https://github.com/ZackPashkin/YOLOv3-EfficientNet-EffYolo
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ZackPashkin/text2cartoon-pytorch-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ZeinabRahbar/Implementation-for-the-paper-of-SEMI-SUPERVISED-CLASSIFICATION-WITH-GRAPH-CONVOLUTIONAL-NETWORKS
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/Zengxianxian727/TPN_paddle
https://github.com/ZhangNYG/BSN
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/ZhangNYG/ST-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/ZhiHanZ/IRIS0-SPAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.