TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation

• URL: http://arxiv.org/abs/2409.05393v1
• Date: Mon, 9 Sep 2024 07:43:58 GMT
• Title: TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation
• Authors: Jiaqi Yang, Ye Huang, Xiangjian He, Linlin Shen, Guoping Qiu,
• Abstract summary: This work proposes a task-adaptive prompt framework based on the Segment Anything Model (SAM) It uses a unique generative approach to prompts alongside a comprehensive model structure and specialized prototype computation. After task-specific and weighted guidance, the abundant feature information of SAM can be better learned for Cross-dominan few-shot segmentation.
• Score: 44.134340976905655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Under the backdrop of large-scale pre-training, large visual models (LVM) have demonstrated significant potential in image understanding. The recent emergence of the Segment Anything Model (SAM) has brought a qualitative shift in the field of image segmentation, supporting flexible interactive cues and strong learning capabilities. However, its performance often falls short in cross-domain and few-shot applications. Transferring prior knowledge from foundation models to new applications while preserving learning capabilities is worth exploring. This work proposes a task-adaptive prompt framework based on SAM, a new paradigm for Cross-dominan few-shot segmentation (CD-FSS). First, a Multi-level Feature Fusion (MFF) was used for integrated feature extraction. Besides, an additional Class Domain Task-Adaptive Auto-Prompt (CDTAP) module was combined with the segmentation branch for class-domain agnostic feature extraction and high-quality learnable prompt production. This significant advancement uses a unique generative approach to prompts alongside a comprehensive model structure and specialized prototype computation. While ensuring that the prior knowledge of SAM is not discarded, the new branch disentangles category and domain information through prototypes, guiding it in adapting the CD-FSS. We have achieved the best results on three benchmarks compared to the recent state-of-the-art (SOTA) methods. Comprehensive experiments showed that after task-specific and weighted guidance, the abundant feature information of SAM can be better learned for CD-FSS.

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