SOURCE-LINKED INTELLIGENCE
Rethinking Camouflage Image Generation towards a Training-Free Paradigm
Camouflage image generation (CIG) aims to synthesize realistic camouflaged images by blending foreground objects into concealment-compatible background contexts. Achieving this objective requires jointly satisfying three coupled requirements: foreground preservation to retain target integrity, semantic compatibility to select plausible concealment contexts, and appearance assimilation to reduce visual discrepancies. Recent approaches predominantly rely on task-specific training on camouflage datasets to address these requirements, incurring substantial computational cost and limiting generaliz
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-13T08:24:45.000Z
First collected: 2026-09-20T12:41:04.663Z. This is not the publication date.