https://github.com/myc159/Deep-Taylor-Decomposition
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
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.
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Leveraging Temporal Contextualization for Video Action Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Semi-Supervised Classification with Graph Convolutional Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Semi-Supervised Classification with Graph Convolutional Networks
Explicit Visual Prompting for Universal Foreground Segmentations
Motion Transformer with Global Intention Localization and Local Movement Refinement
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
Very Deep Convolutional Networks for Large-Scale Image Recognition
Semi-Supervised Classification with Graph Convolutional Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.