用关键点引导提升大模型在隐蔽目标检测中的表现
Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance
- 设计多尺度网络预测候选点处目标存在的概率
- 通过正负点对比策略引导SAM分割，提升检测效果
- 首次实现大模型直接用于隐蔽目标检测，无需从头建模
大模型已成为可适配多种下游任务的新范式，仅需微调即可应用。本文针对隐蔽目标检测（COD）问题，利用通用分割模型SAM进行改进。先前研究认为SAM不适用于COD，但本工作表明：只要合理引导，SAM可有效工作。为此，提出新框架实现点引导增强：首先设计促进点定位网络（PPT-net），利用多尺度特征预测图像上候选点处目标存在的概率；随后设计关键点选择算法（KPS），以正负点对比方式引导SAM进行分割。这是首个将大模型成功应用于COD的工作，在3个数据集、6项指标下均取得优于现有方法的结果。该研究提供了一种开箱即用的COD解决方案，相比从零设计专用模型，不仅性能更优，还将问题转化为寻找有信息量但不要求精确的点引导，显著降低难度。
原文摘要 · Abstract (English)
Big model has emerged as a new research paradigm that can be applied to various down-stream tasks with only minor effort for domain adaption. Correspondingly, this study tackles Camouflaged Object Detection (COD) leveraging the Segment Anything Model (SAM). The previous studies declared that SAM is not workable for COD but this study reveals that SAM works if promoted properly, for which we devise a new framework to render point promotions: First, we develop the Promotion Point Targeting Network (PPT-net) to leverage multi-scale features in predicting the probabilities of camouflaged objects' presences at given candidate points over the image. Then, we develop a key point selection (KPS) algorithm to deploy both positive and negative point promotions contrastively to SAM to guide the segmentation. It is the first work to facilitate big model for COD and achieves plausible results experimentally over the existing methods on 3 data sets under 6 metrics. This study demonstrates an off-the-shelf methodology for COD by leveraging SAM, which gains advantage over designing professional models from scratch, not only in performance, but also in turning the problem to a less challenging task, that is, seeking informative but not exactly precise promotions.
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