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arXiv 2511.19425cs.CV

SAM3-Adapter: 高效适应Segment Anything 3用于伪装物分割、阴影检测和医学图像分割

SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation

KOKONI, Moxin (Huzhou) Tech. Co., LTD（摩西（湖州）科技有限公司）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Information Engineering, Huzhou University（湖州大学信息工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（新加坡南洋理工大学电子与电气工程学院）；College of Computing and Data Science, Nanyang Technological University（新加坡南洋理工大学计算与数据科学学院）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学与技术学院）

Tianrun Chen, Runlong Cao, Xinda Yu, Lanyun Zhu, Chaotao Ding, Deyi Ji, Cheng Chen, Qi Zhu, Chunyan Xu, Papa Mao, Ying Zang

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AI总结：

SAM3-Adapter通过高效适配框架提升SAM3在伪装物分割、阴影检测和医学影像分割等任务中的性能。

摘要：

大规模基础模型的迅速发展已重塑了图像分割的格局，像Segment Anything这样的模型在多样化的视觉任务中实现了前所未有的通用性。然而，先前的版本，包括SAM及其后续版本，仍然在细粒度、低层次的分割挑战上面临困难，例如伪装物检测、医学图像分割、细胞图像分割和阴影检测。为了解决这些限制，我们于2023年最初提出了SAM-Adapter，在这些困难场景中取得了显著的提升。随着Segment Anything 3（SAM3）的出现——一个更高效、性能更高的进化版本，具有重新设计的架构和改进的训练流程——我们重新审视这些长期存在的挑战。在本工作中，我们提出了SAM3-Adapter，这是首个针对SAM3的适配框架，解锁了其完整的分割能力。SAM3-Adapter不仅减少了计算开销，而且在多个下游任务中持续超越SAM和SAM2基于的解决方案，建立了新的最先进的结果，包括医学影像、伪装（隐藏）物分割和阴影检测。基于原始SAM-Adapter的模块化和可组合设计哲学，SAM3-Adapter提供了更强的通用性、更丰富的任务适应性和显著提高的分割精度。广泛的实验证实，将SAM3与我们的适配器结合，其准确性、鲁棒性和效率都优于所有先前的SAM基于的适应方案。我们希望SAM3-Adapter可以为未来的研究和实际分割应用提供基础。代码、预训练模型和数据处理流程均已提供。

英文摘要：

The rapid rise of large-scale foundation models has reshaped the landscape of image segmentation, with models such as Segment Anything achieving unprecedented versatility across diverse vision tasks. However, previous generations-including SAM and its successor-still struggle with fine-grained, low-level segmentation challenges such as camouflaged object detection, medical image segmentation, cell image segmentation, and shadow detection. To address these limitations, we originally proposed SAM-Adapter in 2023, demonstrating substantial gains on these difficult scenarios. With the emergence of Segment Anything 3 (SAM3)-a more efficient and higher-performing evolution with a redesigned architecture and improved training pipeline-we revisit these long-standing challenges. In this work, we present SAM3-Adapter, the first adapter framework tailored for SAM3 that unlocks its full segmentation capability. SAM3-Adapter not only reduces computational overhead but also consistently surpasses both SAM and SAM2-based solutions, establishing new state-of-the-art results across multiple downstream tasks, including medical imaging, camouflaged (concealed) object segmentation, and shadow detection. Built upon the modular and composable design philosophy of the original SAM-Adapter, SAM3-Adapter provides stronger generalizability, richer task adaptability, and significantly improved segmentation precision. Extensive experiments confirm that integrating SAM3 with our adapter yields superior accuracy, robustness, and efficiency compared to all prior SAM-based adaptations. We hope SAM3-Adapter can serve as a foundation for future research and practical segmentation applications. Code, pre-trained models, and data processing pipelines are available.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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