Action Recognition
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shanakarg/stjd-spatio-temporal-joint-density-driven-learning-for-skeleton-based-action-recognition
공식 코드Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition
https://github.com/shanice-l/st-cubism
공식 코드Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
https://github.com/shankarp8/knowledge_distillation
공식 코드https://github.com/shark0-0/vg4d
공식 코드https://github.com/shash29-dev/STEP
공식 코드DeepSTEP: Simultaneous Tracking and Estimation of Pose for Animals and Humans
https://github.com/shibshib/Moe-VRD
공식 코드https://github.com/shiyemin/shuttlenet
공식 코드Learning long-term dependencies for action recognition with a biologically-inspired deep network
https://github.com/shiyin-lc/dd-gcn
공식 코드DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action Recognition
https://github.com/shngjz/wildcamera
공식 코드Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
https://github.com/showlab/egovlp
공식 코드https://github.com/shubham-goel/4D-Humans
공식 코드Humans in 4D: Reconstructing and Tracking Humans with Transformers
https://github.com/silentview/emcid
공식 코드https://github.com/sislab-unitn/dyknow
공식 코드DyKnow: Dynamically Verifying Time-Sensitive Factual Knowledge in LLMs
https://github.com/sitzikbs/3dincaction
공식 코드https://github.com/skelemoa/ntu-x
공식 코드NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human Actions
https://github.com/skelemoa/psumnet
공식 코드PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action Recognition
https://github.com/skelemoa/quovadis
공식 코드https://github.com/skelemoa/synse-zsl
공식 코드Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition
https://github.com/skelemoa/tal-hmo
공식 코드Hear Me Out: Fusional Approaches for Audio Augmented Temporal Action Localization
https://github.com/skylark0924/rofunc
공식 코드https://github.com/sming256/OpenTAD
공식 코드Harnessing Temporal Causality for Advanced Temporal Action Detection
https://github.com/sofiabroome/painface-recognition
공식 코드https://github.com/sofiabroome/temporal-shape-dataset
공식 코드https://github.com/soheilb/multimodal_dictionary_learning
공식 코드Multimodal Task-Driven Dictionary Learning for Image Classification
https://github.com/songxiaoshuai/postedit
공식 코드https://github.com/sperimental3/charon
공식 코드Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
https://github.com/sprkrd/sat_strips_learn
공식 코드https://github.com/srijandas07/VPN
공식 코드VPN: Learning Video-Pose Embedding for Activities of Daily Living
https://github.com/srijandas07/clip_baseline_lta_ego4d
공식 코드Video + CLIP Baseline for Ego4D Long-term Action Anticipation
https://github.com/srijandas07/vpnplusplus
공식 코드VPN++: Rethinking Video-Pose embeddings for understanding Activities of Daily Living
https://github.com/stan-hua/temporal_hydronephrosis
공식 코드https://github.com/stanfordnlp/pyvene
공식 코드pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
https://github.com/steveliao93/gcn_logsigrnn
공식 코드Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition
https://github.com/stnoah1/infogcn2
공식 코드https://github.com/stnoah1/sode
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.