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Temporal Action Localization
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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://github.com/stanfordnlp/pyvene
공식 코드pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
https://github.com/stepankonev/MotionCNN-Waymo-Open-Motion-Dataset
공식 코드MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving
https://github.com/stepankonev/waymo-motion-prediction-chalenge-2022-multipath-plus-plus
공식 코드https://github.com/stergiosbamp/deep-physical-activity-prediction
공식 코드https://github.com/steveliao93/gcn_logsigrnn
공식 코드Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition
https://github.com/stnoah1/infogcn2
공식 코드https://github.com/stnoah1/sode
공식 코드https://github.com/stoa-xh91/acfl
공식 코드Skeleton-based Action Recognition via Adaptive Cross-Form Learning
https://github.com/stuarteiffert/MCTS-GRNN
공식 코드Path Planning in Dynamic Environments using Generative RNNs and Monte Carlo Tree Search
https://github.com/suakaw/bdq_privacyar
공식 코드Privacy-Preserving Action Recognition via Motion Difference Quantization
https://github.com/sudo-Boris/mr-Blip
공식 코드The Surprising Effectiveness of Multimodal Large Language Models for Video Moment Retrieval
https://github.com/suhaisheng/difsd
공식 코드https://github.com/sujoyp/wtalc-pytorch
공식 코드W-TALC: Weakly-supervised Temporal Activity Localization and Classification
https://github.com/sunzhihao18/ForgerySleuth
공식 코드ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
https://github.com/swathikirans/LSTA
공식 코드LSTA: Long Short-Term Attention for Egocentric Action Recognition
https://github.com/swathikirans/gsf
공식 코드https://github.com/swc-17/sparsedrive
공식 코드SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
https://github.com/sycny/rae
공식 코드Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
https://github.com/syon-li/coachhook
공식 코드https://github.com/syscv/trafficbots
공식 코드TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
https://github.com/tabguigui/fiptr
공식 코드https://github.com/tailin1009/dualhead-network
공식 코드Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition
https://github.com/tal-aurorax/scen
공식 코드https://github.com/talalwasim/video-focalnets
공식 코드Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
https://github.com/tanghaoyu258/feel
공식 코드Visual Self-paced Iterative Learning for Unsupervised Temporal Action Localization
https://github.com/tda-jyamiti/d-gril
공식 코드D-GRIL: End-to-End Topological Learning with 2-parameter Persistence
https://github.com/tdavchev/structured-trajectory-prediction
공식 코드https://github.com/tecsar-uncc/adg-pose
공식 코드ADG-Pose: Automated Dataset Generation for Real-World Human Pose Estimation
https://github.com/tedhuang96/mifwlstm
공식 코드Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM
https://github.com/tensorflow/models
공식 코드MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/thanhhff/HOTAD
공식 코드https://github.com/thartvigsen/grace
공식 코드Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
https://github.com/thu-keg/event-level-knowledge-editing
공식 코드https://github.com/thunlp/eren
공식 코드https://github.com/tigerchen52/qrneuron
공식 코드Identifying Query-Relevant Neurons in Large Language Models for Long-Form Texts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.