Bi-CamoDiffusion: A Boundary-informed Diffusion Approach for Camouflaged Object Detection

Abstract:Bi-CamoDiffusion is introduced, an evolution of the CamoDiffusion framework for camouflaged object detection. It integrates edge priors into early-stage embeddings via a parameter-free injection process, enhancing boundary sharpness and preventing structural ambiguity. An optimization objective that unifies spatial accuracy, structural constraints, and uncertainty supervision is also proposed, allowing the model to capture of both the object's global context and its intricate boundary transitions. Evaluations across the CAMO, COD10K, and NC4K datasets show that Bi-CamoDiffusion surpasses the baseline, delivering sharper delineation of thin structures and protrusions while also minimizing false positives. The model consistently outperforms existing state-of-the-art methods across all evaluated metrics, including $S_m$, $F_{\beta}^{w}$, $E_m$, and $MAE$, demonstrating a more precise object-background separation and sharper boundary recovery. Code available at: this https URL

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2603.13357 [cs.CV]

(or arXiv:2603.13357v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2603.13357 Focus to learn more arXiv-issued DOI via DataCite

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1401-1410, 2026

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