4 citations · 12 across the 21 of their papers we have counts for
5 papers · 1 filter
Timestamp-Supervised Action Segmentation from the Perspective of Clustering
Dazhao Du, Enhan Li, Lingyu Si +2
Video action segmentation under timestamp supervision has recently received much attention due to lower annotation costs. Most existing methods generate pseudo-labels for all frame…
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective
Jiangmeng Li, Yanan Zhang, Wenwen Qiang +5
Few-shot learning models learn representations with limited human annotations, and such a learning paradigm demonstrates practicability in various tasks, e.g., image classification…
Robust Causal Graph Representation Learning against Confounding Effects
Hang Gao, Jiangmeng Li, Wenwen Qiang +4
The prevailing graph neural network models have achieved significant progress in graph representation learning. However, in this paper, we uncover an ever-overlooked phenomenon: th…
Do we really need temporal convolutions in action segmentation?
Dazhao Du, Bing Su, Yu Li +3
Action classification has made great progress, but segmenting and recognizing actions from long untrimmed videos remains a challenging problem. Most state-of-the-art methods focus…
Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
Hang Gao, Jiangmeng Li, Wenwen Qiang +3
Recent works explore learning graph representations in a self-supervised manner. In graph contrastive learning, benchmark methods apply various graph augmentation approaches. Howev…