https://github.com/arunos728/SELFY
공식 코드Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition
Papers with Code 아카이브
Activity Recognition
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- 평가표
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- 보존된 평가 기록
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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/arvid-pku/atoke
공식 코드History Matters: Temporal Knowledge Editing in Large Language Model
https://github.com/arvintashakori/SemiPFL
공식 코드SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence
https://github.com/asankagp/UAV-GESTURE
공식 코드UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition
https://github.com/asankagp/mod20
공식 코드A Multi-viewpoint Outdoor Dataset for Human Action Recognition
https://github.com/asdfo123/scedit
공식 코드https://github.com/asheshjain399/RNNexp
공식 코드https://github.com/assembly101/assembly101.github.io
공식 코드Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
https://github.com/au-revoir/model-editing-ft
공식 코드https://github.com/avc2-uestc/wifitad
공식 코드WiFi CSI Based Temporal Activity Detection via Dual Pyramid Network
https://github.com/axd465/PAM_Deep_Learning_Undersampling_Publication
공식 코드Reconstructing undersampled photoacoustic microscopy images using deep learning
https://github.com/azazel7/paper-benchmark
공식 코드A benchmark of data stream classification for human activity recognition on connected objects
https://github.com/azzam-alhussain/fpga-qhar
공식 코드FPGA-QHAR: Throughput-Optimized for Quantized Human Action Recognition on The Edge
https://github.com/azzh1/purls
공식 코드Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
https://github.com/baaivision/eva
공식 코드EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/bahjat-kawar/time-diffusion
공식 코드Editing Implicit Assumptions in Text-to-Image Diffusion Models
https://github.com/baidu-research/vidpress-sports
공식 코드https://github.com/baixianghuang/behavior-edit
공식 코드Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm
https://github.com/bboylyg/expose-before-you-defend
공식 코드Expose Before You Defend: Unifying and Enhancing Backdoor Defenses via Exposed Models
https://github.com/bcv-uniandes/tapir
공식 코드https://github.com/bebr2/editcot
공식 코드https://github.com/beepkh/unike
공식 코드Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration
https://github.com/belindal/LaMPP
공식 코드LaMPP: Language Models as Probabilistic Priors for Perception and Action
https://github.com/berkerdemirel/decompl
공식 코드https://github.com/bernardo-gatto/n-mode-GDS
공식 코드https://github.com/bespontaneous/afnet-pytorch
공식 코드https://github.com/bghojogh/Fisherposes
공식 코드Recognizing Involuntary Actions from 3D Skeleton Data Using Body States
https://github.com/bghojogh/Roweisposes
공식 코드Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition
https://github.com/bhi-research/ava_mdetr
공식 코드Exploring Modulated Detection Transformer as a Tool for Action Recognition in Videos
https://github.com/big-data-lab-team/benchmark-har-data-stream
공식 코드Reducing numerical precision preserves classification accuracy in Mondrian Forests
https://github.com/big-data-lab-team/paper-generalizability-window-size
공식 코드https://github.com/bigai-ai/ICE
공식 코드In-Context Editing: Learning Knowledge from Self-Induced Distributions
https://github.com/blue-yonder/tsfresh
공식 코드Feature engineering workflow for activity recognition from synchronized inertial measurement units
https://github.com/bmartacho/UniPose
공식 코드UniPose: Unified Human Pose Estimation in Single Images and Videos
https://github.com/bochenxie/neurohar
공식 코드Event Voxel Set Transformer for Spatiotemporal Representation Learning on Event Streams
https://github.com/bomri/SlowFast/blob/master/projects/vtn/README.md
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.