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://bitbucket.org/dmoltisanti/action_recognition_single_timestamps
공식 코드Action Recognition from Single Timestamp Supervision in Untrimmed Videos
https://bitbucket.org/sahasuman/bmvc2016_code
공식 코드Online Spatiotemporal Action Detection and Prediction via Causal Representations
https://github.com/0zgur0/STAR_Network
공식 코드https://github.com/11maxed11/phase-based-action-recognition
공식 코드https://github.com/1adrianb/video-transformers
공식 코드https://github.com/1xbq1/a2mc
공식 코드Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning
https://github.com/1xbq1/psp-learning
공식 코드Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action Recognition
https://github.com/2000222/skateboard-ai
공식 코드SkateboardAI: The Coolest Video Action Recognition for Skateboarding
https://github.com/2020aptx4869lm/Forecasting-Human-Object-Interaction-in-FPV
공식 코드https://github.com/2tianyao1/actionllm
공식 코드https://github.com/3dperceptionlab/hierarchicalactionrecognition
공식 코드Enhancing Action Recognition by Leveraging the Hierarchical Structure of Actions and Textual Context
https://github.com/3dperceptionlab/visual-wetlandbirds
공식 코드Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos
https://github.com/3huo/3DV-Action
공식 코드https://github.com/3huo/MVDI
공식 코드Action Recognition for Depth Video using Multi-view Dynamic Images
https://github.com/4dvlab/hucenlife
공식 코드Human-centric Scene Understanding for 3D Large-scale Scenarios
https://github.com/99eren99/dirfiaf
공식 코드https://github.com/99eren99/dis25k
공식 코드https://github.com/ABi-dot/Kedkg
공식 코드Knowledge Editing with Dynamic Knowledge Graphs for Multi-Hop Question Answering
https://github.com/ADiko1997/S-GEAR
공식 코드Semantically Guided Representation Learning For Action Anticipation
https://github.com/AIFARMS/NOR-behavior-recognition
공식 코드Vision-based Behavioral Recognition of Novelty Preference in Pigs
https://github.com/ActionR-G/ActionHSIC
공식 코드https://github.com/AlanZhang1995/TwoStreamSR
공식 코드Two-Stream Action Recognition-Oriented Video Super-Resolution
https://github.com/AlbertoSabater/Skeleton-based-One-shot-Action-Recognition
공식 코드One-shot action recognition in challenging therapy scenarios
https://github.com/Alibaba-MIIL/ImageNet21K
공식 코드https://github.com/Alibaba-MIIL/STAM
공식 코드https://github.com/Andy1621/CT-Net
공식 코드CT-Net: Channel Tensorization Network for Video Classification
https://github.com/Anirudh257/strm
공식 코드Spatio-temporal Relation Modeling for Few-shot Action Recognition
https://github.com/AnyiRao/SceneSeg
공식 코드A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
https://github.com/Aries-chen/MedEditBench
공식 코드Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing
https://github.com/BBYL9413/TDS-CLIP
공식 코드TDS-CLIP: Temporal Difference Side Network for Image-to-Video Transfer Learning
https://github.com/BUPTSJZhang/SiT-MLP
공식 코드https://github.com/BUserName/PGL
공식 코드Source-Free Progressive Graph Learning for Open-Set Domain Adaptation
https://github.com/BestJuly/Inter-intra-video-contrastive-learning
공식 코드Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
https://github.com/BestJuly/Pretext-Contrastive-Learning
공식 코드Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation Leaning
https://github.com/Betswish/Cross-Lingual-Consistency
공식 코드Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.