DGA-Net: Enhancing SAM with Depth Prompting and Graph-Anchor Guidance for Camouflaged Object Detection

• URL: http://arxiv.org/abs/2601.02831v1
• Date: Tue, 06 Jan 2026 09:04:23 GMT
• Title: DGA-Net: Enhancing SAM with Depth Prompting and Graph-Anchor Guidance for Camouflaged Object Detection
• Authors: Yuetong Li, Qing Zhang, Yilin Zhao, Gongyang Li, Zeming Liu,
• Abstract summary: We present DGA-Net, a framework that adapts the Segment Anything Model (SAM) via a novel depth prompting" paradigm.<n>Specifically, we propose a Cross-modal Graph Enhancement (CGE) module that synthesizes RGB semantics and geometric depth within a heterogeneous graph.<n>We also design an Anchor-Guided Refinement (AGR) module to counteract the inherent information decay in feature hierarchies.
• Score: 28.171222957310956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To fully exploit depth cues in Camouflaged Object Detection (COD), we present DGA-Net, a specialized framework that adapts the Segment Anything Model (SAM) via a novel ``depth prompting" paradigm. Distinguished from existing approaches that primarily rely on sparse prompts (e.g., points or boxes), our method introduces a holistic mechanism for constructing and propagating dense depth prompts. Specifically, we propose a Cross-modal Graph Enhancement (CGE) module that synthesizes RGB semantics and depth geometric within a heterogeneous graph to form a unified guidance signal. Furthermore, we design an Anchor-Guided Refinement (AGR) module. To counteract the inherent information decay in feature hierarchies, AGR forges a global anchor and establishes direct non-local pathways to broadcast this guidance from deep to shallow layers, ensuring precise and consistent segmentation. Quantitative and qualitative experimental results demonstrate that our proposed DGA-Net outperforms the state-of-the-art COD methods.

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