Paper Info
Title
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer.
Abstract
Few-shot semantic segmentation intends to predict pixel level categories using only a few labeled samples. Existing few-shot methods focus primarily on the categories sampled from the same distribution. Nevertheless, this assumption cannot always be ensured. The actual domain shift problem significantly reduces the performance of few-shot learning. To remedy this problem, we propose an interesting and challenging cross-domain few-shot semantic segmentation task, where the training and test tasks perform on different domains. Specifically, we first propose a meta-memory bank to improve the generalization of the segmentation network by bridging the domain gap between source and target domains. The meta-memory stores the intra-domain style information from source domain instances and transfers it to target samples. Subsequently, we adopt a new contrastive learning strategy to explore the knowledge of different categories during the training stage. The negative and positive pairs are obtained from the proposed memory-based style augmentation. Comprehensive experiments demonstrate that our proposed method achieves promising results on cross-domain few-shot semantic segmentation tasks on COCO-20, PASCAL-5, FSS-1000, and SUIM datasets.
Year
Venue
DocType
2022
IEEE Conference on Computer Vision and Pattern Recognition
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Wenjian Wang100.34
Lijuan Duan221526.13
Yuxi Wang300.34
Qing En400.34
Junsong Fan562.44
Zhaoxiang Zhang6102299.76