  Camouflaged vision perception is an important vision task with numerous
practical applications. Due to the expensive collection and labeling costs,
this community struggles with a major bottleneck that the species category of
its datasets is limited to a small number of object species. However, the
existing camouflaged generation methods require specifying the background
manually, thus failing to extend the camouflaged sample diversity in a low-cost
manner. In this paper, we propose a Latent Background Knowledge
Retrieval-Augmented Diffusion (LAKE-RED) for camouflaged image generation. To
our knowledge, our contributions mainly include: (1) For the first time, we
propose a camouflaged generation paradigm that does not need to receive any
background inputs. (2) Our LAKE-RED is the first knowledge retrieval-augmented
method with interpretability for camouflaged generation, in which we propose an
idea that knowledge retrieval and reasoning enhancement are separated
explicitly, to alleviate the task-specific challenges. Moreover, our method is
not restricted to specific foreground targets or backgrounds, offering a
potential for extending camouflaged vision perception to more diverse domains.
(3) Experimental results demonstrate that our method outperforms the existing
approaches, generating more realistic camouflage images.
