https://github.com/moein-shariatnia/OpenAI-CLIP
공식 코드ENIGMA-51: Towards a Fine-Grained Understanding of Human-Object Interactions in Industrial Scenarios
Papers with Code 아카이브
Action Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 8
- 평가표
- 11
- 보존된 평가 기록
- 81
Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/moonlight52137/st-vgcn
공식 코드Improving Skeleton-based Action Recognition with Interactive Object Information
https://github.com/musicalOffering/ActionSwitch-release
공식 코드ActionSwitch: Class-agnostic Detection of Simultaneous Actions in Streaming Videos
https://github.com/navidfoumani/tsc_survey
공식 코드Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey
https://github.com/ncgarcia/modality-distillation
공식 코드Modality Distillation with Multiple Stream Networks for Action Recognition
https://github.com/newsdata/SoccerDB
공식 코드SoccerDB: A Large-Scale Database for Comprehensive Video Understanding
https://github.com/ngk03/dl_ppg_review
공식 코드A Review of Deep Learning Methods for Photoplethysmography Data
https://github.com/nipdep/SEZ-HARN
공식 코드SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition Network
https://github.com/nkatzz/OLED
공식 코드Semi-Supervised Online Structure Learning for Composite Event Recognition
https://github.com/nntanaka/Fourier-Analysis-for-Skeleton-based-Action-Recognition
공식 코드https://github.com/noboevbo/ehpi_action_recognition
공식 코드Simple yet efficient real-time pose-based action recognition
https://github.com/okankop/ASDNet
공식 코드How to Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild
https://github.com/ol-mega/ppca
공식 코드Long-term Conversation Analysis: Exploring Utility and Privacy
https://github.com/oliverhxh/skeletongcl
공식 코드Graph Contrastive Learning for Skeleton-based Action Recognition
https://github.com/open-mmlab/mmaction
공식 코드https://github.com/opengvlab/hulk
공식 코드Hulk: A Universal Knowledge Translator for Human-Centric Tasks
https://github.com/opengvlab/humanbench
공식 코드Hulk: A Universal Knowledge Translator for Human-Centric Tasks
https://github.com/ostadabbas/video-based-infant-action-recognition
공식 코드Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art
https://github.com/osvai/ske2grid
공식 코드Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition
https://github.com/oulutan/ACAM_Demo
공식 코드https://github.com/oulutan/ActorConditionedAttentionMaps
공식 코드https://github.com/outsiders17711/e2eet-skeleton-based-hgr-using-data-level-fusion
공식 코드Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN
https://github.com/owenwang517/tvt-2025
공식 코드Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System
https://github.com/pangzhan27/CMeRT
공식 코드Context-Enhanced Memory-Refined Transformer for Online Action Detection
https://github.com/paolomandica/hysp
공식 코드HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations
https://github.com/parhamhadikhani/human-activity-discovery-hpgmk
공식 코드https://github.com/percom-submission/PerCom-2023
공식 코드Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition
https://github.com/pha123661/SA-DVAE
공식 코드https://github.com/philipph77/ts-moco
공식 코드TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
https://github.com/piergiaj/AViD
공식 코드https://github.com/piergiaj/mlb-youtube
공식 코드https://github.com/piergiaj/super-events-cvpr18
공식 코드Learning Latent Super-Events to Detect Multiple Activities in Videos
https://github.com/piergiaj/tgm-icml19
공식 코드https://github.com/plnguyen2908/laser_asd
공식 코드LASER: Lip Landmark Assisted Speaker Detection for Robustness
https://github.com/prijatelj/human-activity-recognition-in-an-open-world
공식 코드https://github.com/ptirupat/MLAD
공식 코드Modeling Multi-Label Action Dependencies for Temporal Action Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.