AoP-SAM: Automation of Prompts for Efficient Segmentation

• URL: http://arxiv.org/abs/2505.11980v1
• Date: Sat, 17 May 2025 12:27:36 GMT
• Title: AoP-SAM: Automation of Prompts for Efficient Segmentation
• Authors: Yi Chen, Mu-Young Son, Chuanbo Hua, Joo-Young Kim,
• Abstract summary: We propose the Automation of Prompts for SAM (AoP-SAM), a novel approach that learns to generate essential prompts in optimal locations automatically.<n>AoP-SAM enhances SAM's efficiency and usability by eliminating manual input, making it better suited for real-world tasks.<n>We introduce a test-time instance-level Adaptive Sampling and Filtering mechanism that generates prompts in a coarse-to-fine manner.
• Score: 4.108113665793567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Segment Anything Model (SAM) is a powerful foundation model for image segmentation, showing robust zero-shot generalization through prompt engineering. However, relying on manual prompts is impractical for real-world applications, particularly in scenarios where rapid prompt provision and resource efficiency are crucial. In this paper, we propose the Automation of Prompts for SAM (AoP-SAM), a novel approach that learns to generate essential prompts in optimal locations automatically. AoP-SAM enhances SAM's efficiency and usability by eliminating manual input, making it better suited for real-world tasks. Our approach employs a lightweight yet efficient Prompt Predictor model that detects key entities across images and identifies the optimal regions for placing prompt candidates. This method leverages SAM's image embeddings, preserving its zero-shot generalization capabilities without requiring fine-tuning. Additionally, we introduce a test-time instance-level Adaptive Sampling and Filtering mechanism that generates prompts in a coarse-to-fine manner. This notably enhances both prompt and mask generation efficiency by reducing computational overhead and minimizing redundant mask refinements. Evaluations of three datasets demonstrate that AoP-SAM substantially improves both prompt generation efficiency and mask generation accuracy, making SAM more effective for automated segmentation tasks.

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