25 citations · 43 across the 7 of their papers we have counts for
4 papers · 2 filters
MUSTER: A Multi-scale Transformer-based Decoder for Semantic Segmentation
Jing Xu, Wentao Shi, Pan Gao +2
In recent works on semantic segmentation, there has been a significant focus on designing and integrating transformer-based encoders. However, less attention has been given to tran…
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
Jing Xu, Xu Luo, Xinglin Pan +3
Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks without sufficient annotations. Despite the emergence of a number of few-shot learning metho…
SSformer: A Lightweight Transformer for Semantic Segmentation
Wentao Shi, Jing Xu, Pan Gao
It is well believed that Transformer performs better in semantic segmentation compared to convolutional neural networks. Nevertheless, the original Vision Transformer may lack of i…
Channel Importance Matters in Few-Shot Image Classification
Xu Luo, Jing Xu, Zenglin Xu
Few-Shot Learning (FSL) requires vision models to quickly adapt to brand-new classification tasks with a shift in task distribution. Understanding the difficulties posed by this ta…