Bi-CamoDiffusion：一种结合边界的扩散方法用于伪装物体检测
Bi-CamoDiffusion: A Boundary-informed Diffusion Approach for Camouflaged Object Detection
AI总结：
Bi-CamoDiffusion通过参数-free注入过程将边缘先验整合到早期嵌入中，提升边界清晰度并减少结构模糊，平衡空间精度、结构约束和不确定性监督，实现更精确的物体-背景分离和边界恢复。
摘要：
Bi-CamoDiffusion是CamoDiffusion框架的进化版本，通过参数-free注入过程将边缘先验整合到早期嵌入中，增强边界清晰度并防止结构模糊。该方法由统一的优化目标驱动，平衡空间精度、结构约束和不确定性监督，使模型能够捕捉物体的全局上下文和复杂边界过渡。在CAMO、COD10K和NC4K基准测试中，Bi-CamoDiffusion在细长结构和突起的界定上表现更清晰，同时减少假阳性。此外，模型在所有评估指标上均优于现有最先进方法，包括$S_m$、$F_β^{w}$、$E_m$和$MAE$，证明了更精确的物体-背景分离和更清晰的边界恢复。
英文摘要：
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 $S_m$, $F_β^{w}$, $E_m$, and $MAE$, demonstrating a more precise object-background separation and sharper boundary recovery. Code available at: https://github.com/plsuarez/Bi-CamoDiffusion
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
