RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network
Xinyang Huang (Beijing University of Posts and Telecommunications),* Chuang Zhu (Beijing University of Posts and Telecommunications ), Wenkai Chen (Beijing University of Posts and Telecommunications)
The 34th British Machine Vision Conference
Abstract
Cross-domain few-shot segmentation (CD-FSS) aims to achieve semantic segmentation in previously unseen domains with a limited number of annotated samples. Although existing CD-FSS models focus on cross-domain feature transformation, relying exclusively on inter-domain knowledge transfer may lead to the loss of critical intra-domain information. To this end, we propose a novel residual transformation network (RestNet) that facilitates knowledge transfer while retaining the intra-domain support-query feature information. Specifically, we propose a Semantic Enhanced Anchor Transform (SEAT) module that maps features to a stable domain-agnostic space using advanced semantics. Additionally, an Intra-domain Residual Enhancement (IRE) module is designed to maintain the intra-domain representation of the original discriminant space in the new space. We also propose a mask prediction strategy based on prototype fusion to help the model gradually learn how to segment. Our RestNet can transfer cross-domain knowledge from both inter-domain and intra-domain without requiring additional fine-tuning. Extensive experiments on ISIC, Chest X-ray, and FSS-1000 show that our RestNet achieves state-of-the-art performance. Our code will be available soon.Video
Citation
@inproceedings{Huang_2023_BMVC,
author = {Xinyang Huang and Chuang Zhu and Wenkai Chen},
title = {RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0012.pdf}
}
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