https://github.com/sisl/gail-driver
Benchmarking Deep Reinforcement Learning for Continuous Control
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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.
Benchmarking Deep Reinforcement Learning for Continuous Control
Learning Transferable Visual Models From Natural Language Supervision
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
JOSENet: A Joint Stream Embedding Network for Violence Detection in Surveillance Videos
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
TSM: Temporal Shift Module for Efficient Video Understanding
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Semi-Supervised Classification with Graph Convolutional Networks
End-to-end Learning of Action Detection from Frame Glimpses in Videos
Learning Transferable Visual Models From Natural Language Supervision
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
AIM: Adapting Image Models for Efficient Video Action Recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Editing Implicit Assumptions in Text-to-Image Diffusion Models
Semi-Supervised Classification with Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.