Scribble-Based Weakly Supervised Camouflaged Object Detection via SAM-Guided Feature Correlation Transformer

Abstract

Weakly Supervised Camouflaged Object Detection (WS-COD) aims to locate camouflaged objects with only sparse supervision, thereby substantially reducing the reliance on costly pixel-level annotations. This task poses two major challenges: limited supervision arising from sparse annotations (e.g., scribbles), and weak discriminability due to the inherent high visual similarity between camouflaged objects and their surroundings. To tackle these challenges, this paper proposes a novel multi-scale feature correlation transformer guided by the Segment Anything Model (SAM) for scribble-based WSCOD. Specifically, we introduce a cross-scale correlation module built upon Transformers, which exploits enriched cross-attention mechanisms to capture long-range global correlations and multi-scale discriminative cues, enabling accurate segmentation of camouflaged objects. In addition, we develop a SAM-based pseudo-label generation module that leverages sparse annotations as prompts to produce high-quality object masks, thereby enhancing supervision. Extensive experiments on three challenging datasets demonstrate that our proposed method consistently and significantly surpasses existing state-of-the-art approaches for scribble-based WSCOD. The code will be available at: http://github.com/ farewellIamLoser/FCT-SAM-WSCOD.