https://github.com/stoa-xh91/acfl
공식 코드Skeleton-based Action Recognition via Adaptive Cross-Form Learning
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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.
Skeleton-based Action Recognition via Adaptive Cross-Form Learning
Towards Improving Spatiotemporal Action Recognition in Videos
Privacy-Preserving Action Recognition via Motion Difference Quantization
EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens
ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
LSTA: Long Short-Term Attention for Egocentric Action Recognition
Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition
Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition
STRIDE: Single-video based Temporally Continuous Occlusion-Robust 3D Pose Estimation
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation
ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation
Bridging Video-text Retrieval with Multiple Choice Questions
LSTC: Boosting Atomic Action Detection with Long-Short-Term Context
MoViNets: Mobile Video Networks for Efficient Video Recognition
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Action Selection Learning for Multi-label Multi-view Action Recognition
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
VideoDG: Generalizing Temporal Relations in Videos to Novel Domains
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.