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Camouflaged Instance Segmentation In-the-Wild: Dataset, Method, and Benchmark Suite.
- Source :
-
IEEE Transactions on Image Processing . 2022, Vol. 31, p287-300. 14p. - Publication Year :
- 2022
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *IMAGE color analysis
*PIXELS
*IMAGE segmentation
Subjects
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 31
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 170077052
- Full Text :
- https://doi.org/10.1109/TIP.2021.3130490