https://github.com/kkanshul/Hide-and-Seek
공식 코드Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyond
Papers with Code 아카이브
Action Localization
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Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/klauscc/tallformer
공식 코드TALLFormer: Temporal Action Localization with a Long-memory Transformer
https://github.com/kmeng01/rome
공식 코드https://github.com/kowoonho/arfree
공식 코드Autoregression-free video prediction using diffusion model for mitigating error propagation
https://github.com/koyumizutani/earthquake
공식 코드https://github.com/kpeng9510/hopadiff
공식 코드https://github.com/kpeng9510/os-sar
공식 코드Navigating Open Set Scenarios for Skeleton-based Action Recognition
https://github.com/kpeng9510/proformer
공식 코드Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
https://github.com/kpeng9510/ravar
공식 코드https://github.com/kpeng9510/trans4soar
공식 코드Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions
https://github.com/kracwarlock/action-recognition-visual-attention
공식 코드https://github.com/krystalan/bi_zsre
공식 코드https://github.com/kth-rpl/detect_vlm
공식 코드Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models
https://github.com/kth-rpl/rmp
공식 코드https://github.com/kwwcv/selfmotion
공식 코드https://github.com/kwwcv/ssmp
공식 코드Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
https://github.com/kylemin/A2CL-PT
공식 코드Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization
https://github.com/l1bra1/rigidflow
공식 코드Self-Supervised 3D Scene Flow Estimation and Motion Prediction using Local Rigidity Prior
https://github.com/lambert-x/video-semisup
공식 코드Learning from Temporal Gradient for Semi-supervised Action Recognition
https://github.com/laurenok24/nsaqa
공식 코드Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
https://github.com/layer6ai-labs/ASL
공식 코드https://github.com/ld-ing/TCFPN-ISBA
공식 코드Weakly-Supervised Action Segmentation with Iterative Soft Boundary Assignment
https://github.com/ldarrick/PathSignatures
공식 코드https://github.com/leiwangr/video-ar
공식 코드https://github.com/leo-liuzy/propmend
공식 코드https://github.com/leob03/mdmp
공식 코드MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertainty
https://github.com/leolyliu/HOI4D-Instructions
공식 코드HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction
https://github.com/leonhlj/fac-net
공식 코드Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization
https://github.com/leonhlj/rskp
공식 코드Weakly Supervised Temporal Action Localization via Representative Snippet Knowledge Propagation
https://github.com/leonlha/Video-Action-Recognition-via-PSO-ConvNet-Transformer-Collaborative-Learning-with-Dynamics
공식 코드Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer
https://github.com/leonlha/video-action-recognition-collaborative-learning-with-dynamics-via-pso-convnet-transformer
공식 코드Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer
https://github.com/levigty/acl
공식 코드Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
https://github.com/levigty/aimclr
공식 코드https://github.com/lgzlilili/biscc
공식 코드Weakly-Supervised Temporal Action Localization with Bidirectional Semantic Consistency Constraint
https://github.com/lgzlilili/boosting-wtal
공식 코드Boosting Weakly-Supervised Temporal Action Localization with Text Information
https://github.com/lihaoxin05/stillmix
공식 코드Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foreground
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.