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/hjjpku/adaptive_sampler
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
https://github.com/hnVfly/pointnet.mxnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/hnmspirit/pointnet1
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/hobincar/SA-LSTM
https://github.com/houshd/LSTM-FCN
https://github.com/houstonsantos/CassavaLeafDisease
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/huggingface/pytorch-image-models
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
https://github.com/huggingface/transformers
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/huguyuehuhu/HCN-pytorch
https://github.com/huynhtuan17ti/AI4VN-Hackathon2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/huyz1117/Non_Local_Net_TensorFlow
https://github.com/huzpsb/EffTransfer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hyang0129/foodclassapp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hyperfraise/Pytorch-StNet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/hyperfraise/StNet
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
https://github.com/hyperfraise/end-to-end-BSN.pytorch
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
https://github.com/hyperparameters/tracking_via_colorization
https://github.com/hz-ants/pointnet.pytorch
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/iamhoushiyou/tsn
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
https://github.com/iamilyasedunov/key_word_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/iamjeff7/j-va-aagcn
View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
https://github.com/iduta/pyconvsegnet
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
https://github.com/iejMac/ScriptWriter
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/if-loops/adaptive-selective-synaptic-dampening
Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening
https://github.com/imanlab/deep_movement_primitives
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/innat/UniFormerV2
https://github.com/innat/Video-FocalNets
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
https://github.com/innat/VideoMAE
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
https://github.com/innat/VideoSwin
https://github.com/isaachaw/GrabCarRecognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/isotlaboratory/ml4vrp
https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
https://github.com/jachiam/cpo
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/jacksonchen1998/Cold-Start-Reinforcement-Learning-with-Softmax-Policy-Gradient
https://github.com/jacobgil/pytorch-grad-cam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.