43 citations · 131 across the 23 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
cs.CV2019★ 1 cited
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
Da Chen, Yongliang Yang, Zunlei Feng +6
Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class. Recent advances benefit from the met…
cs.CV2019
Defensive Few-shot Learning
Wenbin Li, Lei Wang, Xingxing Zhang +4
This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the exis…
cs.CV2019★ 43 cited
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning
Wenbin Li, Lei Wang, Jinglin Xu +3
Few-shot learning in image classification aims to learn a classifier to classify images when only few training examples are available for each class. Recent work has achieved promi…