https://github.com/yz-cnsdqz/TemporalActionParsing-FineGrained
Temporal Convolutional Networks for Action Segmentation and Detection
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.
Temporal Convolutional Networks for Action Segmentation and Detection
Frontal Low-rank Random Tensors for Fine-grained Action Segmentation
Towards Active Vision for Action Localization with Reactive Control and Predictive Learning
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Scene Transformer: A unified architecture for predicting multiple agent trajectories
Very Deep Convolutional Networks for Large-Scale Image Recognition
VideoLSTM Convolves, Attends and Flows for Action Recognition
Semi-Supervised Classification with Graph Convolutional Networks
Moments in Time Dataset: one million videos for event understanding
Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity
Very Deep Convolutional Networks for Large-Scale Image Recognition
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
DF-Net: The Digital Forensics Network for Image Forgery Detection
DF-Net: The Digital Forensics Network for Image Forgery Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.