RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network

• URL: http://arxiv.org/abs/2308.13469v2
• Date: Thu, 14 Sep 2023 01:13:21 GMT
• Title: RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network
• Authors: Xinyang Huang, Chuang Zhu, Wenkai Chen
• Abstract summary: Cross-domain few-shot segmentation (CD-FSS) aims to achieve semantic segmentation in previously unseen domains with a limited number of annotated samples. We propose a novel residual transformation network (RestNet) that facilitates knowledge transfer while retaining the intra-domain support-Query feature information.
• Score: 4.232614032390374
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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