Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite

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 in-the-wild, we introduce a new 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 methods on our newly constructed CAMO++ dataset in various scenarios. We also propose Camouflage Fusion Learning (CFL) framework for camouflaged instance segmentation to further improve the state-of-the-art performance. The dataset, models, evaluation suite, and benchmark will be publicly available at our project page: this https URL