Details

Autor(en) / Beteiligte

• Le, Trung-Nghia
• Cao, Yubo
• Nguyen, Tan-Cong
• Le, Minh-Quan
• Nguyen, Khanh-Duy
• Do, Thanh-Toan
• Tran, Minh-Triet
• Nguyen, Tam V.

Titel

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

Ist Teil von

• IEEE transactions on image processing, 2022, Vol.31, p.287-300

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2022

Quelle

IEEE Xplore

Beschreibungen/Notizen

• This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends 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 present an extensive evaluation of state-of-the-art instance segmentation methods on our newly constructed CAMO++ dataset in various scenarios. We also present a camouflage fusion learning (CFL) framework for camouflaged instance segmentation to further improve the performance of state-of-the-art methods. The dataset, model, evaluation suite, and benchmark will be made publicly available on our project page.