Camouflaged Instance Segmentation: Dataset and Benchmark Suite.
Trung-Nghia Le, Yubo Cao, Tan-Cong Nguyen, Khanh-Duy Nguyen, Thanh-Toan Do, Minh–Triet Tran, Tam Nguyen · arXiv (Cornell University) · 2021
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.