https://github.com/yijieyang23/skelet-gcn
공식 코드Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformation
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformation
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Kronecker Mask and Interpretive Prompts are Language-Action Video Learners
Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning
CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences
Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization
Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention
Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
TempCLR: Temporal Alignment Representation with Contrastive Learning
Motion-driven Visual Tempo Learning for Video-based Action Recognition
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial Sensors
First and Second Order Dynamics in a Hierarchical SOM system for Action Recognition
Hierarchical growing grid networks for skeleton based action recognition
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)
Fine-grained Affordance Annotation for Egocentric Hand-Object Interaction Videos
Interpreting Arithmetic Mechanism in Large Language Models through Comparative Neuron Analysis
Anticipative Feature Fusion Transformer for Multi-Modal Action Anticipation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.