Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910902490497024 |
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| author | Chen, Pei-Chi Yao, Yi Hsu, Chan-Feng Xie, HongXia Chen, Hung-Jen Shuai, Hong-Han Cheng, Wen-Huang |
| author_facet | Chen, Pei-Chi Yao, Yi Hsu, Chan-Feng Xie, HongXia Chen, Hung-Jen Shuai, Hong-Han Cheng, Wen-Huang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02180 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation Chen, Pei-Chi Yao, Yi Hsu, Chan-Feng Xie, HongXia Chen, Hung-Jen Shuai, Hong-Han Cheng, Wen-Huang Computer Vision and Pattern Recognition I.4.0; I.4.8; I.2.10 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. |
| title | Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation |
| topic | Computer Vision and Pattern Recognition I.4.0; I.4.8; I.2.10 |
| url | https://arxiv.org/abs/2504.02180 |