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

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 datase...

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Bibliographic Details
Published in:IEEE transactions on image processing 2022, Vol.31, p.287-300
Main Authors: Le, Trung-Nghia, Cao, Yubo, Nguyen, Tan-Cong, Le, Minh-Quan, Nguyen, Khanh-Duy, Do, Thanh-Toan, Tran, Minh-Triet, Nguyen, Tam V.
Format: Article
Language:English
Subjects:
benchmark suite
Benchmark testing
Benchmarks
Camouflage
camouflage dataset
Camouflaged instance segmentation
Datasets
Image color analysis
Image segmentation
in-the-wild image
Instance segmentation
multimodal learning
Object segmentation
Semantics
State-of-the-art reviews
Task analysis
Urban areas
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Summary: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.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2021.3130490