16 citations · 25 across the 4 of their papers we have counts for
5 papers · 1 filter
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 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…
Instance Credibility Inference for Few-Shot Learning
Yikai Wang, Chengming Xu, Chen Liu +2
Few-shot learning (FSL) aims to recognize new objects with extremely limited training data for each category. Previous efforts are made by either leveraging meta-learning paradigm…
Coherent and Controllable Outfit Generation
Kedan Li, Chen Liu, David Forsyth
When thinking about dressing oneself, people often have a theme in mind whether they're going to a tropical getaway or wish to appear attractive at a cocktail party. A useful outfi…