TSAL: Few-shot Text Segmentation Based on Attribute Learning
To address the scarcity of high-quality annotations and the high cost of pixel-level labeling in scene text segmentation, this paper proposes TSAL, a few-shot learning framework. TSAL leverages CLIP’s vision-language priors via a dual-branch architecture: a vision-guided branch extracts spatial features, while an adaptive prompt-guided branch models textual semantics. We introduce the Adaptive Feature Alignment (AFA) module—the first to enable learnable attribute tokens to dynamically align with both visual features and prompt prototypes. This constitutes the first systematic application of attribute learning to few-shot text segmentation. Evaluated under few-shot settings across multiple benchmarks, TSAL achieves state-of-the-art performance using only a minimal number of support samples, significantly improving segmentation accuracy and cross-instance generalization for text regions.