Papers with Code 아카이브
Action Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Image source: The Kinetics Human Action Video Dataset
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/4paradigm-cv/se-stad
공식 코드A Simple and Efficient Pipeline to Build an End-to-End Spatial-Temporal Action Detector
https://github.com/AICPS/roadscene2vec
공식 코드roadscene2vec: A Tool for Extracting and Embedding Road Scene-Graphs
https://github.com/AIS-Bonn/lidar_person_action_detection
공식 코드https://github.com/Alibaba-MIIL/STAM
공식 코드https://github.com/Alvin-Zeng/PGCN
공식 코드Graph Convolutional Networks for Temporal Action Localization
https://github.com/Andy1621/CT-Net
공식 코드CT-Net: Channel Tensorization Network for Video Classification
https://github.com/Cogito2012/OpenTAL
공식 코드https://github.com/ControlNet/MARLIN
공식 코드MARLIN: Masked Autoencoder for facial video Representation LearnINg
https://github.com/DAVEISHAN/TCLR
공식 코드TCLR: Temporal Contrastive Learning for Video Representation
https://github.com/HJYao00/Side4Video
공식 코드Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer Learning
https://github.com/HumamAlwassel/TSP
공식 코드TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks
https://github.com/IBM/bLVNet-TAM
공식 코드https://github.com/InnovArul/vidreid_cosegmentation
공식 코드Co-segmentation Inspired Attention Module for Video-based Computer Vision Tasks
https://github.com/LukasHedegaard/co3d
공식 코드Continual 3D Convolutional Neural Networks for Real-time Processing of Videos
https://github.com/MCG-NJU/CPD-Video
공식 코드Learning Spatiotemporal Features via Video and Text Pair Discrimination
https://github.com/MCG-NJU/TDN
공식 코드TDN: Temporal Difference Networks for Efficient Action Recognition
https://github.com/MCG-NJU/VideoMAE
공식 코드VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MCG-NJU/VideoMAE-Action-Detection
공식 코드VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/MIT-HAN-LAB/temporal-shift-module
공식 코드TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/MehwishG/T3D-CNN
공식 코드Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework
https://github.com/MichiganCOG/M-PACT
공식 코드M-PACT: An Open Source Platform for Repeatable Activity Classification Research
https://github.com/Microsoft/EdgeML
공식 코드FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
https://github.com/OFA-Sys/ONE-PEACE
공식 코드ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
https://github.com/OpenGVLab/VideoMAEv2
공식 코드VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
https://github.com/PaddlePaddle/models
공식 코드Deep Concept-wise Temporal Convolutional Networks for Action Localization
https://github.com/ParitoshParmar/HalluciNet
공식 코드HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/ParitoshParmar/MTL-AQA
공식 코드What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/Pilhyeon/WTAL-Uncertainty-Modeling
공식 코드Weakly-supervised Temporal Action Localization by Uncertainty Modeling
https://github.com/RS2002/CrossFi
공식 코드CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network
https://github.com/RS2002/KNN-MMD
공식 코드KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment
https://github.com/SwinTransformer/Video-Swin-Transformer
공식 코드https://github.com/TengdaHan/MemDPC
공식 코드Memory-augmented Dense Predictive Coding for Video Representation Learning
https://github.com/The-Martyr/OccludeNet-Dataset
공식 코드OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusions
https://github.com/VUT-HFUT/MiGA2023_Track1
공식 코드Joint Skeletal and Semantic Embedding Loss for Micro-gesture Classification
https://github.com/YangDi666/UNIK
공식 코드UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.