Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance

TL;DR

This paper introduces a novel framework that enhances the Segment Anything Model (SAM) for camouflaged object detection by using a key point-based guidance approach, significantly improving performance on multiple datasets.

Contribution

It is the first to adapt SAM for camouflaged object detection through a new promotion point targeting network and key point selection algorithm, enabling effective big model utilization.

Findings

Achieves plausible results on 3 datasets under 6 metrics.

Demonstrates the effectiveness of point promotions in guiding SAM for COD.

Provides an off-the-shelf methodology that outperforms existing models.

Abstract

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.