https://github.com/rijuldhir/TSM
Temporal Segment Networks: Towards Good Practices for Deep 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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Learning Transferable Visual Models From Natural Language Supervision
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
Benchmarking Deep Reinforcement Learning for Continuous Control
Benchmarking Deep Reinforcement Learning for Continuous Control
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Action Genome: Actions as Composition of Spatio-temporal Scene Graphs
Benchmarking Deep Reinforcement Learning for Continuous Control
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
Learning Transferable Visual Models From Natural Language Supervision
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Temporal Convolutional Networks for Action Segmentation and Detection
Learning Transferable Visual Models From Natural Language Supervision
Learning Transferable Visual Models From Natural Language Supervision
Video Classification with Channel-Separated Convolutional Networks
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.