https://github.com/Lechatelia/action-proposal-generation
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
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Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
Semi-Supervised Classification with Graph Convolutional Networks
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Efficient Video Action Detection with Token Dropout and Context Refinement
No Time to Waste: Squeeze Time into Channel for Mobile Video Understanding
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
pyannote.audio: neural building blocks for speaker diarization
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.