57 citations · 79 across the 4 of their papers we have counts for
4 papers
Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data
Jie Zhang, Yuhan Li, Yude Wang +2
Few-shot segmentation (FSS) aims to train a model which can segment the object from novel classes with a few labeled samples. The insufficient generalization ability of models lead…
Towards Robust Semantic Segmentation against Patch-based Attack via Attention Refinement
Zheng Yuan, Jie Zhang, Yude Wang +2
The attention mechanism has been proven effective on various visual tasks in recent years. In the semantic segmentation task, the attention mechanism is applied in various methods,…
Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
Yude Wang, Jie Zhang, Meina Kan +2
Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. Most of advanced solutions exploit class activation map (…
Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation
Yude Wang, Jie Zhang, Meina Kan +2
Weakly supervised semantic segmentation has attracted much research interest in recent years considering its advantage of low labeling cost. Most of the advanced algorithms follow…