Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TIP.2021.3130490 | IEEE Transactions on Image Processing |
Keywords | DocType | Volume |
Camouflaged instance segmentation,in-the-wild image,camouflage dataset,benchmark suite,multimodal learning | Journal | 31 |
Issue | ISSN | Citations |
1 | 1057-7149 | 0 |
PageRank | References | Authors |
0.34 | 9 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Trung-Nghia Le | 1 | 0 | 0.34 |
| Yubo Cao | 2 | 0 | 0.34 |
| Tan-Cong Nguyen | 3 | 1 | 1.70 |
| Minh-Quan Le | 4 | 1 | 2.09 |
| Khanh-Duy Nguyen | 5 | 0 | 0.34 |
| Thanh-Toan Do | 6 | 0 | 0.34 |
| Minh-Triet Tran | 7 | 0 | 0.68 |
| Tam Van Nguyen | 8 | 206 | 18.38 |