16 citations · 51 across the 9 of their papers we have counts for
10 papers
Towards Reliable and Holistic Visual In-Context Learning Prompt Selection
Wenxiao Wu, Jing-Hao Xue, Chengming Xu +5
Visual In-Context Learning (VICL) has emerged as a prominent approach for adapting visual foundation models to novel tasks, by effectively exploiting contextual information embedde…
Towards Global Optimal Visual In-Context Learning Prompt Selection
Chengming Xu, Chen Liu, Yikai Wang +2
Visual In-Context Learning (VICL) is a prevailing way to transfer visual foundation models to new tasks by leveraging contextual information contained in in-context examples to enh…
Split-PU: Hardness-aware Training Strategy for Positive-Unlabeled Learning
Chengming Xu, Chen Liu, Siqian Yang +4
Positive-Unlabeled (PU) learning aims to learn a model with rare positive samples and abundant unlabeled samples. Compared with classical binary classification, the task of PU lear…
PatchMix Augmentation to Identify Causal Features in Few-shot Learning
Chengming Xu, Chen Liu, Xinwei Sun +4
The task of Few-shot learning (FSL) aims to transfer the knowledge learned from base categories with sufficient labelled data to novel categories with scarce known information. It…
Learning to Learn and Sample BRDFs
Chen Liu, Michael Fischer, Tobias Ritschel
We propose a method to accelerate the joint process of physically acquiring and learning neural Bi-directional Reflectance Distribution Function (BRDF) models. While BRDF learning…
Learning Dynamic Alignment via Meta-filter for Few-shot Learning
Chengming Xu, Chen Liu, Li Zhang +5
Few-shot learning (FSL), which aims to recognise new classes by adapting the learned knowledge with extremely limited few-shot (support) examples, remains an important open problem…