Textual and Visual Guided Task Adaptation for Source-Free Cross-Domain Few-Shot Segmentation

• URL: http://arxiv.org/abs/2508.05213v1
• Date: Thu, 07 Aug 2025 09:48:24 GMT
• Title: Textual and Visual Guided Task Adaptation for Source-Free Cross-Domain Few-Shot Segmentation
• Authors: Jianming Liu, Wenlong Qiu, Haitao Wei,
• Abstract summary: Few-Shot(FSS) aims to efficient segmentation of new objects with few labeled samples.<n>Cross-Domain Few-Shot(CD-FSS) is proposed to mitigate such performance degradation.
• Score: 0.979247551980983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-Shot Segmentation(FSS) aims to efficient segmentation of new objects with few labeled samples. However, its performance significantly degrades when domain discrepancies exist between training and deployment. Cross-Domain Few-Shot Segmentation(CD-FSS) is proposed to mitigate such performance degradation. Current CD-FSS methods primarily sought to develop segmentation models on a source domain capable of cross-domain generalization. However, driven by escalating concerns over data privacy and the imperative to minimize data transfer and training expenses, the development of source-free CD-FSS approaches has become essential. In this work, we propose a source-free CD-FSS method that leverages both textual and visual information to facilitate target domain task adaptation without requiring source domain data. Specifically, we first append Task-Specific Attention Adapters (TSAA) to the feature pyramid of a pretrained backbone, which adapt multi-level features extracted from the shared pre-trained backbone to the target task. Then, the parameters of the TSAA are trained through a Visual-Visual Embedding Alignment (VVEA) module and a Text-Visual Embedding Alignment (TVEA) module. The VVEA module utilizes global-local visual features to align image features across different views, while the TVEA module leverages textual priors from pre-aligned multi-modal features (e.g., from CLIP) to guide cross-modal adaptation. By combining the outputs of these modules through dense comparison operations and subsequent fusion via skip connections, our method produces refined prediction masks. Under both 1-shot and 5-shot settings, the proposed approach achieves average segmentation accuracy improvements of 2.18\% and 4.11\%, respectively, across four cross-domain datasets, significantly outperforming state-of-the-art CD-FSS methods. Code are available at https://github.com/ljm198134/TVGTANet.

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