Papers with Code 아카이브
3D Action Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Image: Rahmani et al
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch
https://github.com/mcgridles/LENS
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/mdeff/paper-cnn-graph-nips2016
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
https://github.com/meliketoy/graph-cnn.pytorch
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/memory-attention-networks/MANs
Memory Attention Networks for Skeleton-based Action Recognition
https://github.com/mengxingshifen1218/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/merazlab/3D_Deep_Learning_Link
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/mindspore-ai/models/tree/master/official/gnn/gat
https://github.com/mindspore-ai/models/tree/master/research/cv/tsm
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/minhncedutw/pointnet1_keras
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/mitya8128/experiments_notes
https://github.com/mlzxzhou/keras-gnm
https://github.com/mpuu00001/Siformer
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition
https://github.com/mzolfaghari/chained-multistream-networks
https://github.com/natepuppy/HCN-pytorch
https://github.com/nddoshi/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/negarhdr/PGCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/neilsong/slt
https://github.com/nieci2024/pyglcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/nifangbaage/explict-visual-prompt
Explicit Visual Prompting for Universal Foreground Segmentations
https://github.com/nikitakaraevv/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/niveditarahurkar/CS231N-ActionRecognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
https://github.com/nnaakkaaii/g2-MLP
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/noahtren/Graph-Attention-Networks-TensorFlow-2
https://github.com/opeco17/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/open-mmlab/mmaction2
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/open-mmlab/mmflow
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/open-mmlab/mmskeleton
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/openmoss/language-model-saes
Sparse Autoencoders Find Highly Interpretable Features in Language Models
https://github.com/pablopardod/dyalyt
https://github.com/pairlab/v-cdn
Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/pasrichashivam/Convnets_Grad-CAM_Grad-CAM-PlusPlus_Keras_tensorflow-2
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/paty0504/SIGNTEGRATE
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
https://github.com/pengsl-lab/mssl
https://github.com/pengsl-lab/mssl2drug
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.