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/eliasboughosn/Spatial-Temporal-Graph-Convolutional-Networks
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/elliottd/compareImageDescriptionMeasures
https://github.com/emartinezs44/SparkGCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/encounter1997/fp-detr
https://github.com/epic-kitchens/C1-Action-Recognition-TSN-TRN-TSM
https://github.com/epic-kitchens/annotations
https://github.com/epic-kitchens/epic-kitchens-55-action-models
https://github.com/epic-kitchens/epic-kitchens-55-annotations
https://github.com/epic-kitchens/epic-kitchens-slowfast
https://github.com/epoc88/PFLD_68pts_Pytorch_2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/eps696/aphantasia
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/eric-mitchell/serac
https://github.com/ericksiavichay/cs230-final-project
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/ericyinyzy/vlattack
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/eth212/Qmind_ModelNet10_PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ethanluoyc/transporter-pytorch
Unsupervised Learning of Object Keypoints for Perception and Control
https://github.com/event-ahu/safe_largevlm
https://github.com/facebookresearch/ClassyVision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/facebookresearch/Listen-to-Look
https://github.com/facebookresearch/brainmagick
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/facebookresearch/clip-rocket
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/facebookresearch/detectron
https://github.com/facebookresearch/hiervl
https://github.com/facebookresearch/pycls
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/facebookresearch/pytorchvideo
https://github.com/facebookresearch/xformers
Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers
https://github.com/fandulu/Keras-for-Co-occurrence-Feature-Learning-from-Skeleton-Data-for-Action-Recognition
https://github.com/fanzhenliu/dagad
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/farazahmeds/Classification-of-brain-tumor-using-Spatiotemporal-models
ConvNet Architecture Search for Spatiotemporal Feature Learning
https://github.com/fastscience-ai/medflamingo
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/fdsa1860/skeletal
https://github.com/federicopozzi33/MobileOne-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/fiabdu/Commonly-Interesting-Images
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/filaPro/visda2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.