  Text-to-image diffusion techniques have shown exceptional capability of
producing high-quality images from text descriptions. This indicates that there
exists a strong correlation between the visual and textual domains. In
addition, text-image discriminative models such as CLIP excel in image
labelling from text prompts, thanks to the rich and diverse information
available from open concepts. In this paper, we leverage these technical
advances to solve a challenging problem in computer vision: camouflaged
instance segmentation. Specifically, we propose a method built upon a
state-of-the-art diffusion model, empowered by open-vocabulary to learn
multi-scale textual-visual features for camouflaged object representations.
Such cross-domain representations are desirable in segmenting camouflaged
objects where visual cues are subtle to distinguish the objects from the
background, especially in segmenting novel objects which are not seen in
training. We also develop technically supportive components to effectively fuse
cross-domain features and engage relevant features towards respective
foreground objects. We validate our method and compare it with existing ones on
several benchmark datasets of camouflaged instance segmentation and generic
open-vocabulary instance segmentation. Experimental results confirm the
advances of our method over existing ones. We will publish our code and
pre-trained models to support future research.
