Cross-domain Few-shot Segmentation with Transductive Fine-tuning

• URL: http://arxiv.org/abs/2211.14745v1
• Date: Sun, 27 Nov 2022 06:44:41 GMT
• Title: Cross-domain Few-shot Segmentation with Transductive Fine-tuning
• Authors: Yuhang Lu, Xinyi Wu, Zhenyao Wu, Song Wang
• Abstract summary: We propose to transductively fine-tune the base model on a set of query images under the few-shot setting. Our method could consistently and significantly improve the performance of prototypical FSS models in all cross-domain tasks.
• Score: 29.81009103722184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot segmentation (FSS) expects models trained on base classes to work on novel classes with the help of a few support images. However, when there exists a domain gap between the base and novel classes, the state-of-the-art FSS methods may even fail to segment simple objects. To improve their performance on unseen domains, we propose to transductively fine-tune the base model on a set of query images under the few-shot setting, where the core idea is to implicitly guide the segmentation of query images using support labels. Although different images are not directly comparable, their class-wise prototypes are desired to be aligned in the feature space. By aligning query and support prototypes with an uncertainty-aware contrastive loss, and using a supervised cross-entropy loss and an unsupervised boundary loss as regularizations, our method could generalize the base model to the target domain without additional labels. We conduct extensive experiments under various cross-domain settings of natural, remote sensing, and medical images. The results show that our method could consistently and significantly improve the performance of prototypical FSS models in all cross-domain tasks.

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