45 citations · 105 across the 10 of their papers we have counts for
4 papers · 1 filter
Exploring Category-correlated Feature for Few-shot Image Classification
Jing Xu, Xinglin Pan, Xu Luo +2
Few-shot classification aims to adapt classifiers to novel classes with a few training samples. However, the insufficiency of training data may cause a biased estimation of feature…
Adaptive Multi-layer Contrastive Graph Neural Networks
Shuhao Shi, Pengfei Xie, Xu Luo +4
We present Adaptive Multi-layer Contrastive Graph Neural Networks (AMC-GNN), a self-supervised learning framework for Graph Neural Network, which learns feature representations of…
Boosting Few-Shot Classification with View-Learnable Contrastive Learning
Xu Luo, Yuxuan Chen, Liangjian Wen +2
The goal of few-shot classification is to classify new categories with few labeled examples within each class. Nowadays, the excellent performance in handling few-shot classificati…
Rectifying the Shortcut Learning of Background for Few-Shot Learning
Xu Luo, Longhui Wei, Liangjian Wen +4
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL). In this paper, we for the first time…