Papers with Code 아카이브
Temporal Action Localization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://bitbucket.org/columbiadvmm/cdc
공식 코드https://bitbucket.org/dmoltisanti/action_recognition_single_timestamps
공식 코드Action Recognition from Single Timestamp Supervision in Untrimmed Videos
https://github.com/11maxed11/phase-based-action-recognition
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/3dlg-hcvc/OPD
공식 코드https://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/705062791/pgbig
공식 코드https://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/ActionR-G/ActionHSIC
공식 코드https://github.com/AdaCompNUS/GAMMA
공식 코드https://github.com/AlanZhang1995/TwoStreamSR
공식 코드Two-Stream Action Recognition-Oriented Video Super-Resolution
https://github.com/AliaksandrSiarohin/monkey-net
공식 코드https://github.com/Alvin-Zeng/PGCN
공식 코드Graph Convolutional Networks for Temporal Action Localization
https://github.com/Amir-Samadi/VVF-TP
공식 코드https://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/BII-wushuang/Lie-Group-Motion-Prediction
공식 코드Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction
https://github.com/BUPTSJZhang/SiT-MLP
공식 코드https://github.com/BarqueroGerman/BeLFusion
공식 코드BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction
https://github.com/Betswish/Cross-Lingual-Consistency
공식 코드Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models
https://github.com/BoPang1996/Semi-Coupled-Structure-for-visual-sequental-tasks
공식 코드Complex Sequential Understanding through the Awareness of Spatial and Temporal Concepts
https://github.com/BobXWu/AKEW
공식 코드https://github.com/CHELSEA234/SkelNet_motion_prediction
공식 코드Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies
https://github.com/CMU-CREATE-Lab/deep-smoke-machine
공식 코드https://github.com/CMU-CREATE-Lab/video-labeling-tool
공식 코드https://github.com/CV-Lehigh/Bias_IMD
공식 코드https://github.com/Chain123/Meta-HAR
공식 코드Meta-HAR: Federated Representation Learning for Human Activity Recognition
https://github.com/Charrrrrlie/SGA-INTERACT
공식 코드https://github.com/Chiaraplizz/ST-TR
공식 코드Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.