https://github.com/linziyi96/st-adapter
공식 코드ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition
Integrating Human Parsing and Pose Network for Human Action Recognition
High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action Recognition
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
Should We Really Edit Language Models? On the Evaluation of Edited Language Models
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition
Source-Free Progressive Graph Learning for Open-Set Domain Adaptation
Decomposing and Editing Predictions by Modeling Model Computation
Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recogntion
Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recogntion
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation
Masked Motion Predictors are Strong 3D Action Representation Learners
Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
Generative Action Description Prompts for Skeleton-based Action Recognition
Generative Action Description Prompts for Skeleton-based Action Recognition
Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition
Interaction Relational Network for Mutual Action Recognition
MGSampler: An Explainable Sampling Strategy for Video Action Recognition
ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.