Action Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 15
- 평가표
- 56
- 보존된 평가 기록
- 650
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/MekkaSiekka/C3D-UCF11-Tensorflow
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/Melo-Wang/Deep-Learning-on-Point-Cloud-of-Aerospace-Components-for-3D-Classification
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Messham87/COVID_GAT
https://github.com/Michaelgod/test
Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/MichiganCOG/M-PACT
Learning Spatiotemporal Features with 3D Convolutional Networks
https://github.com/MikeG112/RFSBD
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/Mind23-2/MindCode-101/tree/main/I3D
https://github.com/Mind23-2/MindCode-101/tree/main/MVD
https://github.com/Mind23-2/MindCode-101/tree/main/Non_local
https://github.com/Mind23-2/MindCode-11
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/Mind23-2/MindCode-3/tree/main/I3D
https://github.com/Mind23-2/MindCode-3/tree/main/MVD
https://github.com/Mind23-2/MindCode-37
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Mind23-2/MindCode-4/tree/main/gat
https://github.com/Mind23-2/MindCode-49
https://github.com/Mind23-2/MindCode-5/tree/main/psenet
https://github.com/Mind23-2/MindCode-50
https://github.com/Mind23-2/MindCode-62
Video Classification with Channel-Separated Convolutional Networks
https://github.com/Mind23-2/MindCode-63
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Mind23-2/MindCode-87
https://github.com/Mind23-2/MindCode-88/tree/main/slowfast
https://github.com/MindCode-4/code-1/tree/main/videomae
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MindCode-4/code-11/tree/main/delving-deeper-into-convolutional
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/MindCode-4/code-13/tree/main/refining-activation
https://github.com/MindCode-4/code-13/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindCode-4/code-3/tree/main/florence2
https://github.com/MindCode-4/code-5/tree/main/timesformer
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/MindCode-4/code-5/tree/main/vit_mae
https://github.com/MindCode-4/code-6/tree/main/dice-loss-for-data-imbalanced-nlp-tasks
Delving Deeper into Convolutional Networks for Learning Video Representations
https://github.com/MindCode-4/code-8/tree/main/refining-activation
https://github.com/MindCode-4/code-9/tree/main/vat
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/MindSpore-paper-code-2/code2/tree/main/Non_local
https://github.com/MindSpore-paper-code-2/code2/tree/main/r2plus1d
Video Classification with Channel-Separated Convolutional Networks
https://github.com/MindSpore-paper-code-2/code3/tree/main/stgcn
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
https://github.com/MindSpore-paper-code-2/code3/tree/main/stnet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.