Generative One-shot Camouflage Instance Segmentation

Identifying camouflaged instances is a critical yet underexplored problem in computer vision, where traditional segmentation models often fail due to extreme visual similarity between foreground and background. While recent advances have shown promise with deep learning models, they heavily depend on large annotated datasets, which are costly and impractical to collect in camouflage scenarios. In this work, we tackle this limitation by introducing a novel framework, dubbed CAMO-GenOS, that leverages one-shot annotated samples to drive a generative process for data enrichment. Our approach integrates prompt-guided and mask-conditioned generative mechanisms to synthesize diverse, high-fidelity camouflaged instances, thereby enhancing the learning capacity of segmentation models under minimal supervision. We demonstrate the effectiveness of our CAMO-GenOS by setting up a novel state-of-the-art baseline for one-shot camouflage instance segmentation research on the challenging CAMO-FS benchmark. Code can be found at https://github.com/danhntd/CAMO-GenOS.