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/liluxuan1997/DPP
공식 코드https://github.com/lily2lab/deepssm
공식 코드DeepSSM: Deep State-Space Model for 3D Human Motion Prediction
https://github.com/limaosen0/AS-GCN
공식 코드Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
https://github.com/linghaochan/humanmac
공식 코드HumanMAC: Masked Motion Completion for Human Motion Prediction
https://github.com/lingjun123/single-frame-tal
공식 코드Dilation-Erosion for Single-Frame Supervised Temporal Action Localization
https://github.com/lingo-iitgn/xme
공식 코드https://github.com/linqinliang/decowa
공식 코드Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping
https://github.com/lins-lab/collabedit
공식 코드CollabEdit: Towards Non-destructive Collaborative Knowledge Editing
https://github.com/linziyi96/st-adapter
공식 코드ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
https://github.com/liqiang0307/cltdr-gmg
공식 코드Temporal Action Localization with Cross Layer Task Decoupling and Refinement
https://github.com/liujf69/MMCL-Action
공식 코드Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition
https://github.com/liujf69/icmew2024-track10
공식 코드HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action Recognition
https://github.com/liuqi-creat/sigformer
공식 코드SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation
https://github.com/liuyuancv/WTAL_blessing
공식 코드https://github.com/lizaowo/hpi-gcn
공식 코드High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/lizhi1104/haan
공식 코드Weakly-Supervised Temporal Action Detection for Fine-Grained Videos with Hierarchical Atomic Actions
https://github.com/lliutianc/roselora
공식 코드https://github.com/llm-editing/HalluEditBench
공식 코드https://github.com/longzhong-lin/eda
공식 코드EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
https://github.com/lovelyqian/AMeFu-Net
공식 코드Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
https://github.com/lqinfdim/editingevaluation
공식 코드Should We Really Edit Language Models? On the Evaluation of Edited Language Models
https://github.com/lshiwjx/2s-AGCN
공식 코드Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
https://github.com/lshiwjx/DSTA-Net
공식 코드Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition
https://github.com/lsy0882/RDFA-S6
공식 코드Enhancing Temporal Action Localization: Advanced S6 Modeling with Recurrent Mechanism
https://github.com/ltcontext/ltcontext
공식 코드How Much Temporal Long-Term Context is Needed for Action Segmentation?
https://github.com/lukashedegaard/continual-skeletons
공식 코드https://github.com/luyg45/hardnoboxattack
공식 코드https://github.com/lyhisme/dest
공식 코드A Decoupled Spatio-Temporal Framework for Skeleton-based Action Segmentation
https://github.com/m-hasan-n/roundabout
공식 코드https://github.com/machelreid/editpro
공식 코드https://github.com/maclong01/STEP-CATFormer
공식 코드https://github.com/madrylab/modelcomponents
공식 코드Decomposing and Editing Predictions by Modeling Model Computation
https://github.com/magnux/MotionGAN
공식 코드https://github.com/mail-research/cad-attribution4diffusion
공식 코드https://github.com/maoyunyao/cmd
공식 코드CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.