提升伪装图像生成中前景与背景的协调性与真实感。
Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation
- 引入前景感知特征融合模块，增强前景与背景信息整合。
- 设计前景感知去噪损失，显著提升小物体生成保真度。
- 适合关注伪装视觉、图像生成质量的研究者使用。
伪装图像生成为解决伪装视觉感知中的数据稀缺问题提供了低成本替代方案，避免了繁琐的数据采集与标注。当前最先进的方法仅依赖前景对象生成伪装图像，但仍存在两大缺陷：一是背景知识未能有效融入前景特征，导致前景与背景缺乏一致性（如颜色不匹配）；二是生成过程未优先保证前景对象的保真度，尤其对小目标造成明显失真。为此，我们提出前景感知伪装图像生成模型（FACIG）。具体地，引入前景感知特征融合模块（FAFIM），强化前景特征与背景知识的整合；同时设计前景感知去噪损失，增强对前景重建的监督。在多个数据集上的实验表明，该方法在整体伪装图像质量与前景保真度上均优于先前方法。
原文摘要 · Abstract (English)
Camouflaged image generation is emerging as a solution to data scarcity in camouflaged vision perception, offering a cost-effective alternative to data collection and labeling. Recently, the state-of-the-art approach successfully generates camouflaged images using only foreground objects. However, it faces two critical weaknesses: 1) the background knowledge does not integrate effectively with foreground features, resulting in a lack of foreground-background coherence (e.g., color discrepancy); 2) the generation process does not prioritize the fidelity of foreground objects, which leads to distortion, particularly for small objects. To address these issues, we propose a Foreground-Aware Camouflaged Image Generation (FACIG) model. Specifically, we introduce a Foreground-Aware Feature Integration Module (FAFIM) to strengthen the integration between foreground features and background knowledge. In addition, a Foreground-Aware Denoising Loss is designed to enhance foreground reconstruction supervision. Experiments on various datasets show our method outperforms previous methods in overall camouflaged image quality and foreground fidelity.
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