Papers with Code 아카이브
Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/pso-convnet-dynamics
공식 코드PSO-Convolutional Neural Networks with Heterogeneous Learning Rate
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/lfd/rl_for_jo
공식 코드Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation
https://github.com/lhc1224/OSAD_Net
공식 코드https://github.com/lihaoxin05/stillmix
공식 코드Mitigating and Evaluating Static Bias of Action Representations in the Background and the Foreground
https://github.com/liluxuan1997/DPP
공식 코드https://github.com/lilygeorgescu/ubnormal
공식 코드UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection
https://github.com/limaosen0/AS-GCN
공식 코드Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
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/linuxsino/arr
공식 코드From Recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation
https://github.com/linziyi96/st-adapter
공식 코드ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
https://github.com/liu-zhy/TANet
공식 코드https://github.com/liu-zhy/temporal-adaptive-module
공식 코드https://github.com/liujf69/EPP-Net-Action
공식 코드https://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/liujf69/ipp-net-parsing
공식 코드Integrating Human Parsing and Pose Network for Human Action Recognition
https://github.com/liujianzhi/echoreel
공식 코드https://github.com/lizaowo/hpi-gcn
공식 코드High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/lliutianc/roselora
공식 코드https://github.com/llm-editing/HalluEditBench
공식 코드https://github.com/longbai1006/ossar
공식 코드OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery
https://github.com/lou1sm/online_hard_clustering
공식 코드Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation
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/lukashedegaard/continual-skeletons
공식 코드https://github.com/luoyadan/sf-pgl
공식 코드Source-Free Progressive Graph Learning for Open-Set Domain Adaptation
https://github.com/luyg45/hardnoboxattack
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.