100 citations · 193 across the 12 of their papers we have counts for
5 papers · 1 filter
OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection
Changsheng Lu, Zheyuan Liu, Piotr Koniusz
Exploiting the foundation models (e.g., CLIP) to build a versatile keypoint detector has gained increasing attention. Most existing models accept either the text prompt (e.g., ``th…
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…
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…
Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species
Changsheng Lu, Piotr Koniusz
Current non-rigid object keypoint detectors perform well on a chosen kind of species and body parts, and require a large amount of labelled keypoints for training. Moreover, their…