100 citations · 193 across the 12 of their papers we have counts for
4 papers · 1 filter
Few-shot Shape Recognition by Learning Deep Shape-aware Features
Wenlong Shi, Changsheng Lu, Ming Shao +3
Traditional shape descriptors have been gradually replaced by convolutional neural networks due to their superior performance in feature extraction and classification. The state-of…
Understanding and Mitigating Hyperbolic Dimensional Collapse in Graph Contrastive Learning
Yifei Zhang, Hao Zhu, Menglin Yang +4
Learning generalizable self-supervised graph representations for downstream tasks is challenging. To this end, Contrastive Learning (CL) has emerged as a leading approach. The embe…
Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement
Hao Zhu, Piotr Koniusz
Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared with inductive few-shot learning, transductive models typically perform better as they lev…
From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detection
Changsheng Lu, Hao Zhu, Piotr Koniusz
Unlike current deep keypoint detectors that are trained to recognize limited number of body parts, few-shot keypoint detection (FSKD) attempts to localize any keypoints, including…