https://github.com/zhenghuatan/gmm-ubm_map_sv
공식 코드rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
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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.
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
Boundary Discretization and Reliable Classification Network for Temporal Action Detection
Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
Learning Discriminative Representations for Skeleton Based Action Recognition
Class-incremental Learning for Time Series: Benchmark and Evaluation
MARINE: A Computer Vision Model for Detecting Rare Predator-Prey Interactions in Animal Videos
SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation
End-to-end speaker segmentation for overlap-aware resegmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Semi-Supervised Classification with Graph Convolutional Networks
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Semi-Supervised Classification with Graph Convolutional Networks
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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
Context-Aware RCNN: A Baseline for Action Detection in Videos
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
Context-Aware RCNN: A Baseline for Action Detection in Videos
Semi-Supervised Classification with Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.