  This paper pushes the envelope on camouflaged regions to decompose them into
meaningful components, namely, camouflaged instances. To promote the new task
of camouflaged instance segmentation, we introduce a new large-scale dataset,
namely CAMO++, by extending our preliminary CAMO dataset (camouflaged object
segmentation) in terms of quantity and diversity. The new dataset substantially
increases the number of images with hierarchical pixel-wise ground-truths. We
also provide a benchmark suite for the task of camouflaged instance
segmentation. In particular, we conduct extensive evaluation of
state-of-the-art instance segmentation detectors on our newly constructed
CAMO++ dataset in various scenarios. The dataset, evaluation suite, and
benchmark will be publicly available at our project page.
