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PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation (2401.13051v1)

Published 23 Jan 2024 in cs.CV and eess.IV

Abstract: The Segment Anything Model (SAM) has exhibited outstanding performance in various image segmentation tasks. Despite being trained with over a billion masks, SAM faces challenges in mask prediction quality in numerous scenarios, especially in real-world contexts. In this paper, we introduce a novel prompt-driven adapter into SAM, namely Prompt Adapter Segment Anything Model (PA-SAM), aiming to enhance the segmentation mask quality of the original SAM. By exclusively training the prompt adapter, PA-SAM extracts detailed information from images and optimizes the mask decoder feature at both sparse and dense prompt levels, improving the segmentation performance of SAM to produce high-quality masks. Experimental results demonstrate that our PA-SAM outperforms other SAM-based methods in high-quality, zero-shot, and open-set segmentation. We're making the source code and models available at https://github.com/xzz2/pa-sam.

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References (26)
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Authors (7)
  1. Zhaozhi Xie (4 papers)
  2. Bochen Guan (10 papers)
  3. Weihao Jiang (12 papers)
  4. Muyang Yi (1 paper)
  5. Yue Ding (49 papers)
  6. Hongtao Lu (76 papers)
  7. Lei Zhang (1689 papers)
Citations (8)
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Summary

PA-SAM: Advancing the Segment Anything Model for High-Quality Image Segmentation

The paper "PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation" introduces an innovative approach to enhance the performance of the Segment Anything Model (SAM) in delivering superior image segmentation results. SAM, a foundational model in the domain, is celebrated for its adaptability across a range of tasks but encounters difficulties when tasked with high-quality segmentation due to its often coarse mask boundaries and detail mispredictions. This paper addresses these shortcomings by integrating a prompt-driven adapter—termed Prompt Adapter Segment Anything Model (PA-SAM).

Improvements and Methodology

PA-SAM focuses on refining the SAM framework by introducing a prompt adapter that is exclusively trained, allowing it to harness intricate details from images to optimize mask decoder features. This enhancement seeks to ameliorate SAM's segmentation masks, especially when executing high-quality, zero-shot, and open-set segmentation tasks.

The methodological framework of PA-SAM is multifaceted. Key components include:

  1. Adaptive Detail Enhancement: This is achieved through Dense Prompt Compensation and Sparse Prompt Optimization, integrating rich image details into the segmentation process. These enhancements allow the model to focus on the fine textures and boundaries within images that SAM originally overlooks.
  2. Hard Point Mining: This innovative technique employs a Gumbel top-k mechanism for the dynamic selection of challenging points, translating into a more guided model response to difficult segmentation areas.
  3. Prompt Adapter Integration: The prompt adapter is embedded within the SAM architecture in parallel with the mask decoder, refining the segmentation capability without detrimental changes to SAM's baseline architecture.

Experimental Results

The empirical validation of PA-SAM is conducted on several high-quality segmentation datasets, including DIS, ThinObject-5K, COIFT, and HR-SOD. PA-SAM significantly outperforms both the original SAM and other SAM-adaptation methods. Specifically, PA-SAM achieves notable improvements of 1.7% in mean Intersection over Union (mIoU) and 2.7% in boundary mIoU (BmIoU) compared to previous leading models. This advancement underscores the efficacy of PA-SAM's approach in delivering detailed segmentation masks with precise boundary delineations and feature capture.

Further, PA-SAM maintains its robust performance in zero-shot scenarios, as evidenced by its application to datasets such as COCO, demonstrating the model's resilience and adaptability in diverse segmentation contexts. The model’s performance is graphically detailed, showing the improved ability to segment objects with high precision even in the presence of confusing backgrounds or objects.

Implications and Future Directions

The introduction of PA-SAM has meaningful implications for both the theoretical landscape and practical applications of image segmentation. The advancement illustrates the benefits of finely-tuned integration of prompts within existing architectures, suggesting a pathway to refine and expand the abilities of foundational models like SAM.

The innovative use of prompt adapters and detail mining techniques marks a critical step forward in the detailed understanding and segmentation of imagery. As these methods evolve, they offer potential applications across various domains such as medical imaging, autonomous driving, and complex image editing tasks. Future exploration might consider expanding PA-SAM's architecture to incorporate additional multi-modal inputs or refining the computational efficiency to further bolster its applicability in resource-constrained environments. Furthermore, exploring the integration of LLMs could augment PA-SAM by providing contextual understanding, enhancing both interpretability and accuracy in zero-shot settings.

In conclusion, PA-SAM represents a marked development in image segmentation tasks, offering a promising framework for detailed and fine-grained segmentation outputs. The exploration of prompt-driven upgrades provides insightful avenues for future advancements within this continually evolving field.

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  1. GitHub - xzz2/pa-sam: PA-SAM: Prompt Adapter SAM for High-quality Image Segmentation (84 stars)
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