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

2025-05-14

AI Summary

This work addresses the challenge of directly applying Segment Anything Model (SAM) to camouflaged object detection (COD), where SAM’s generic priors fail to capture subtle camouflage patterns. We propose a lightweight, interpretable keypoint-guided framework: (1) a multi-scale probabilistic prompt localization network (PPT-Net) that predicts salient keypoints; and (2) a contrastive keypoint selection (KPS) algorithm that identifies discriminative positive/negative prompts for zero-shot adaptation of SAM. By integrating multi-scale feature encoding with point-level existence probability modeling, our method enhances SAM’s fine-grained segmentation capability for COD. Evaluated on three standard COD benchmarks across six metrics, our approach consistently outperforms state-of-the-art methods. Results demonstrate SAM’s effective, generalizable, and interpretable “plug-and-play” applicability to COD—establishing a novel paradigm for adapting foundation models to low-resource, high-difficulty vision tasks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Enhancing SAM for camouflaged object detection via key points
• Developing PPT-net and KPS to guide SAM segmentation
• Leveraging big models for COD without custom designs

Innovation

• Uses Segment Anything Model (SAM) for COD
• Develops PPT-net for multi-scale feature prediction
• Implements KPS algorithm for contrastive point guidance