Action Recognition
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-paper-code-2/code3/tree/main/tsn
https://github.com/MindSpore-paper-code-2/code400/tree/main/Efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code10/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/MindSpore-paper-code-3/code2/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code4/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/MindSpore-paper-code-3/code6/tree/main/AVA_cifar
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/MindSpore-paper-code-3/code6/tree/main/BMN
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/MindSpore-paper-code-3/code6/tree/main/C3D
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
https://github.com/MindSpore-paper-code-3/code7/tree/main/flownet2
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/dpn
https://github.com/MindSpore-paper-code-3/code8/tree/main/ecolite
ECO: Efficient Convolutional Network for Online Video Understanding
https://github.com/MindSpore-paper-code-3/code8/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet2
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MindSpore-scientific-2/code-10/tree/main/TSA_mindspore
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
https://github.com/MindSpore-scientific-2/code-12/tree/main/LGL-INR-master
https://github.com/MindSpore-scientific-2/code-3/tree/main/temporal-and-aspectual-entailment
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU
https://github.com/MindSpore-scientific-2/code-8/tree/main/pytorch_convgru
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/MindSpore-scientific-2/code-8/tree/main/temporal-normalizing-flows
Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
https://github.com/MindSpore-scientific/code-1/tree/main/Simba
Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos
https://github.com/MindSpore-scientific/code-10/tree/main/Simba
Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos
https://github.com/MindSpore-scientific/code-11/tree/main/pytorch_convgru
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction
https://github.com/MindSpore-scientific/code-13/tree/main/TSA_mindspore
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
https://github.com/MindSpore-scientific/code-13/tree/main/pytorch_convgru
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction
https://github.com/MindSpore-scientific/code-13/tree/main/token_learner
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MindSpore-scientific/code-14/tree/main/refining-activation
https://github.com/MindSpore-scientific/code-14/tree/main/temporal-and-aspectual-entailment
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/MindSpore-scientific/code-2/tree/main/temporal-and-aspectual-entailment
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/MindSpore-scientific/code-5/tree/main/refining-activation
https://github.com/MindSpore-scientific/code-7/tree/main/VideoMAE
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/ModelBunker/PointNet-TensorFlow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MohammadRezaQaderi/Video-Swin-Transformer
https://github.com/Moon-Xuezha/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.