BiPrompt-SAM: Enhancing Image Segmentation via Explicit Selection between Point and Text Prompts

• URL: http://arxiv.org/abs/2503.19769v2
• Date: Wed, 30 Apr 2025 14:42:34 GMT
• Title: BiPrompt-SAM: Enhancing Image Segmentation via Explicit Selection between Point and Text Prompts
• Authors: Suzhe Xu, Jialin Peng, Chengyuan Zhang,
• Abstract summary: BiPrompt-SAM is a novel dual-modal prompt segmentation framework.<n>It fuses spatial precision and semantic context without complex model modifications.<n>It achieves strong zero-shot performance on the Endovis17 medical dataset.
• Score: 2.7218660375779513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmentation is a fundamental task in computer vision, with prompt-driven methods gaining prominence due to their flexibility. The Segment Anything Model (SAM) excels at point-prompted segmentation, while text-based models, often leveraging powerful multimodal encoders like BEIT-3, provide rich semantic understanding. However, effectively combining these complementary modalities remains a challenge. This paper introduces BiPrompt-SAM, a novel dual-modal prompt segmentation framework employing an explicit selection mechanism. We leverage SAM's ability to generate multiple mask candidates from a single point prompt and use a text-guided mask (generated via EVF-SAM with BEIT-3) to select the point-generated mask that best aligns spatially, measured by Intersection over Union (IoU). This approach, interpretable as a simplified Mixture of Experts (MoE), effectively fuses spatial precision and semantic context without complex model modifications. Notably, our method achieves strong zero-shot performance on the Endovis17 medical dataset (89.55% mDice, 81.46% mIoU) using only a single point prompt per instance. This significantly reduces annotation burden compared to bounding boxes and aligns better with practical clinical workflows, demonstrating the method's effectiveness without domain-specific training. On the RefCOCO series, BiPrompt-SAM attained 87.1%, 86.5%, and 85.8% IoU, significantly outperforming existing approaches. Experiments show BiPrompt-SAM excels in scenarios requiring both spatial accuracy and semantic disambiguation, offering a simple, effective, and interpretable perspective on multi-modal prompt fusion.

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