Segment Anything Model (SAM) has made great progress in anomaly segmentation tasks due to its impressive generalization ability. However, existing methods that directly apply SAM through prompting often overlook the domain shift issue, where SAM performs well on natural images but struggles in industrial scenarios. Parameter-Efficient Fine-Tuning (PEFT) offers a promising solution, but it may yield suboptimal performance by not adequately addressing the perception challenges during adaptation to anomaly images. In this paper, we propose a novel Self-Perceptinon Tuning (SPT) method, aiming to enhance SAM's perception capability for anomaly segmentation. The SPT method incorporates a self-drafting tuning strategy, which generates an initial coarse draft of the anomaly mask, followed by a refinement process. Additionally, a visual-relation-aware adapter is introduced to improve the perception of discriminative relational information for mask generation. Extensive experimental results on several benchmark datasets demonstrate that our SPT method can significantly outperform baseline methods, validating its effectiveness. Models and codes will be available online.

翻译：Segment Anything Model (SAM) 凭借其卓越的泛化能力，在异常分割任务中取得了显著进展。然而，现有通过提示直接应用SAM的方法往往忽视了领域偏移问题：SAM在自然图像上表现优异，但在工业场景中却面临挑战。参数高效微调（PEFT）提供了一种有前景的解决方案，但它在适应异常图像时未能充分应对感知挑战，可能导致次优性能。本文提出了一种新颖的自感知调优（SPT）方法，旨在增强SAM在异常分割中的感知能力。SPT方法采用了一种自草拟调优策略，首先生成异常掩码的初始粗稿，随后进行细化过程。此外，我们引入了一个视觉关系感知适配器，以提升对掩码生成所需的判别性关系信息的感知能力。在多个基准数据集上的大量实验结果表明，我们的SPT方法能够显著超越基线方法，验证了其有效性。模型与代码将在线公开。