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/NEUdeep/MOC-Detector-Pytorch1.4
https://github.com/NVIDIA/DeepLearningExamples
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/NVIDIA/flownet2-pytorch
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
https://github.com/NVlabs/STEP
STEP: Spatio-Temporal Progressive Learning for Video Action Detection
https://github.com/NYU-DICE-Lab/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Negus25/GAT-Mindspore
https://github.com/NicolaBernini/PaperAnalysis_ICLR2021_NegativeDataAugmentation
https://github.com/Nikhil9786/Scene-Segmentation-Using-MovieScenes-Dataset
A Local-to-Global Approach to Multi-modal Movie Scene Segmentation
https://github.com/Nortinwell/TSN
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/OML-Team/open-metric-learning
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/OanaIgnat/i3d_keras
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/Ontheway361/BSN_pytorch
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/OpenGVLab/UniFormerV2
https://github.com/OpenXAIProject/LRP_for_LSTM_Korean_dataset
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
https://github.com/PIX2NVS/NVS_FeatureLearning
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing
https://github.com/PPPrior/i3d-pytorch
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/PaParaZz1/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/PaParaZz1/TemporalShiftModule
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PASSL
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_CLIP_model.md
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/PaddlePaddle/PaddleClas
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/PaddlePaddle/PaddleDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/PaddlePaddle/PaddleMIX/tree/develop/paddlemix/examples
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
https://github.com/PaddlePaddle/PaddleSeg
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/en/model_zoo/recognition/videoswin.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/localization/bmn.md
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsm.md
TSM: Temporal Shift Module for Efficient Video Understanding
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/pp-tsn.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/slowfast.md
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/stgcn.md
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/PaddlePaddle/PaddleVideo/blob/develop/docs/zh-CN/model_zoo/recognition/timesformer.md
Is Space-Time Attention All You Need for Video Understanding?
https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/video/models/bsn
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/PaddlePaddle/models/tree/develop/dygraph/bmn
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
https://github.com/ParitoshParmar/C3D-LSTM--PyTorch
https://github.com/ParitoshParmar/HalluciNet--PyTorch
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.