Search Results for author:
Found 5 papers, 3 papers with code
Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data
Few-shot segmentation (FSS) aims to train a model which can segment the object from novel classes with a few labeled samples.
Towards Robust Semantic Segmentation against Patch-based Attack via Attention Refinement
The attention mechanism has been proven effective on various visual tasks in recent years.
Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic Segmentation
Firstly, we introduce a class-aware cross entropy (CCE) loss for network training.
Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation
Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
Self-supervised Scale Equivariant Network for Weakly Supervised Semantic Segmentation
This regularized CAM can be embedded in most recent advanced weakly supervised semantic segmentation framework.
