Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation

• URL: http://arxiv.org/abs/2504.02180v1
• Date: Wed, 02 Apr 2025 23:51:13 GMT
• Title: Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation
• Authors: Pei-Chi Chen, Yi Yao, Chan-Feng Hsu, HongXia Xie, Hung-Jen Chen, Hong-Han Shuai, Wen-Huang Cheng,
• Abstract summary: We propose a Foreground-Aware Camouflaged Image Generation (FACIG) model to generate camouflaged images.<n> Specifically, we introduce a Foreground-Aware Feature Integration Module (FAFIM) to strengthen the integration between foreground features and background knowledge.<n> Experiments on various datasets show our method outperforms previous methods in overall camouflaged image quality and foreground fidelity.
• Score: 28.86420429221175
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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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