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/HuCui2022/DSTSA-GCN_Gesture
공식 코드https://github.com/HumamAlwassel/XDC
공식 코드Self-Supervised Learning by Cross-Modal Audio-Video Clustering
https://github.com/IBM/RegionViT
공식 코드RegionViT: Regional-to-Local Attention for Vision Transformers
https://github.com/IBM/action-recognition-pytorch
공식 코드Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
https://github.com/IBM/bLVNet-TAM
공식 코드https://github.com/IIT-PAVIS/subspace-clustering-action-recognition
공식 코드Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning
https://github.com/IMSY-DKFZ/self-distilled-swin
공식 코드https://github.com/IkeaASM/IKEA_ASM_Dataset
공식 코드The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose
https://github.com/IuliaDuta/RSTG
공식 코드https://github.com/JHang2020/HiCLR
공식 코드https://github.com/JHang2020/Shap-Mix
공식 코드Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition
https://github.com/Jeongseungwoo/Efficient-Continuous-Manifold-Learning
공식 코드Deep Efficient Continuous Manifold Learning for Time Series Modeling
https://github.com/JerryYLi/Dataset-REPAIR
공식 코드https://github.com/Jho-Yonsei/HD-GCN
공식 코드Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/Jho-Yonsei/STC-Net
공식 코드Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition
https://github.com/KAIST-VICLab/SkateFormer
공식 코드SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition
https://github.com/KAIST-VICLab/TDSM
공식 코드TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition
https://github.com/KaiyangZhou/Dassl.pytorch
공식 코드https://github.com/KimManjin/RSA
공식 코드Relational Self-Attention: What's Missing in Attention for Video Understanding
https://github.com/Kwanss/PCLNet
공식 코드Unsupervised Learning for Optical Flow Estimation Using Pyramid Convolution LSTM
https://github.com/L-KID/Full-Video-Action-Recognition
공식 코드https://github.com/LanglandsLin/MS2L
공식 코드MS$^2$L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
https://github.com/LeiWangR/HDG
공식 코드A Comparative Review of Recent Kinect-based Action Recognition Algorithms
https://github.com/Levigty/CMCS
공식 코드Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
https://github.com/LinguoLi/CrosSCLR
공식 코드3D Human Action Representation Learning via Cross-View Consistency Pursuit
https://github.com/Lyz1213/BadEdit
공식 코드https://github.com/MCG-NJU/AWT
공식 코드AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation
https://github.com/MCG-NJU/CPD-Video
공식 코드Learning Spatiotemporal Features via Video and Text Pair Discrimination
https://github.com/MCG-NJU/CRCNN-Action
공식 코드Context-Aware RCNN: A Baseline for Action Detection in Videos
https://github.com/MCG-NJU/MOC-Detector
공식 코드https://github.com/MCG-NJU/TDN
공식 코드TDN: Temporal Difference Networks for Efficient Action Recognition
https://github.com/MCG-NJU/VideoMAE
공식 코드VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MCG-NJU/VideoMAE-Action-Detection
공식 코드VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MIT-HAN-LAB/temporal-shift-module
공식 코드TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/MMKE-Bench-ICLR/MMKE-Bench
공식 코드MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.