https://github.com/fchollet/deep-learning-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Very Deep Convolutional Networks for Large-Scale Image Recognition
A Closer Look at Spatiotemporal Convolutions for Action Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions
Unsupervised learning of action classes with continuous temporal embedding
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Boundary-sensitive Pre-training for Temporal Localization in Videos
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
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Riemannian Multinomial Logistics Regression for SPD Neural Networks
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Scene Transformer: A unified architecture for predicting multiple agent trajectories
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.