Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation
This addresses data scarcity in camouflaged vision perception by improving image generation, but it is incremental as it builds on existing state-of-the-art approaches.
The paper tackles the problem of generating camouflaged images with poor foreground-background coherence and foreground fidelity, proposing a Foreground-Aware Camouflaged Image Generation (FACIG) model that outperforms previous methods in overall image quality and foreground fidelity.
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.