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 V. Nguyen

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.

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