https://github.com/filipbasara0/simple-clip
Learning Transferable Visual Models From Natural Language Supervision
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
Learning Transferable Visual Models From Natural Language Supervision
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion
A Closer Look at Spatiotemporal Convolutions for Action Recognition
Temporal Binary Representation for Event-Based Action Recognition
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
Learning Transferable Visual Models From Natural Language Supervision
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Riemannian Multinomial Logistics Regression for SPD Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.