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/Parth27/ActionRecognitionVideos
https://github.com/PaulEmmanuelSotir/DeepCV
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/PeterWang512/FALdetector
https://github.com/Phimos/Paddle-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/PotatoSpudowski/CactiNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Powercoder64/TAA-GCN
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
https://github.com/PumpkinYing/GAT
https://github.com/QUVA-Lab/timeception
https://github.com/RemyMess/MMVRC_ICCV_2021_Skeleton_based_Action_Recognition
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles
https://github.com/RichardChangCA/CSI_5386_NLP_Project
https://github.com/RonyAbecidan/Mantranet-pytorch
https://github.com/RoshanRane/segmentation-moving-MNIST
https://github.com/SBPL-Cruz/perch_pose_sampler
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/STAIR-Lab-CIT/STAIR-actions
https://github.com/SY-Xuan/pointnet_paddle
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/SforAiDl/CountCLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/SforAiDl/vformer
https://github.com/ShadowShadowWong/update-pointnet-work
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ShiYaya/video-classification-3d-cnn-pytorch
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/ShobhitMaheshwari/sign-language1
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/SifatMd/Research-Papers
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SilviaLauraPintea/DejaVu
https://github.com/SilvioGiancola/SoccerNet-code
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
https://github.com/SmartPorridge/google-AVA-Dataset-downloader
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
https://github.com/SonuDileep/3-D-Object-Detection-using-PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/SsnL/moco
https://github.com/SsnL/moco_align_uniform
https://github.com/StanfordVL/RubiksNet
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/StanfordVL/moma
https://github.com/StefOe/indrnn-pytorch
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
https://github.com/Stepphonwol/my_yowo
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
https://github.com/Subrata11/IoT-based-Real-time-Human-Motion-Recognition-Based-on-Skeletons-
https://github.com/Sun1992/SSTCN-for-SLR
https://github.com/SunDoge/efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SunnyHaze/ManTraNet-Pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.