TPRNet: camouflaged object detection via transformer-induced progressive refinement network
Camouflaged object detection (COD) is a challenging task which aims to detect objects similar to the surrounding environment. In this paper, we propose a transformer-induced progressive refinement network ( TPRNet ) to solve challenging COD tasks. Specifically, our network includes a Transformer-ind...
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| Published in | The Visual computer Vol. 39; no. 10; pp. 4593 - 4607 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2023
Springer Nature B.V |
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| Abstract | Camouflaged object detection (COD) is a challenging task which aims to detect objects similar to the surrounding environment. In this paper, we propose a transformer-induced progressive refinement network (
TPRNet
) to solve challenging COD tasks. Specifically, our network includes a Transformer-induced Progressive Refinement Module (TPRM) and a Semantic-Spatial Interaction Enhancement Module (SIEM). In TPRM, high-level features with rich semantic information are integrated through transformers as prior guidance, and then, it is sent to the refinement concurrency unit (RCU), and the accurately positioned feature area is obtained through a progressive refinement strategy. In SIEM, we perform feature interaction to localized-accurate semantic features and low-level features to obtain rich fine-grained clues and increase the symbolic power of boundary features. Extensive experiments on four widely used benchmark datasets (i.e., CAMO, CHAMELEON, COD10K, and NC4K) demonstrate that our
TPRNet
is an effective COD model and outperforms state-of-the-art models. The code is available
https://github.com/zhangqiao970914/TPRNet
. |
|---|---|
| AbstractList | Camouflaged object detection (COD) is a challenging task which aims to detect objects similar to the surrounding environment. In this paper, we propose a transformer-induced progressive refinement network (TPRNet) to solve challenging COD tasks. Specifically, our network includes a Transformer-induced Progressive Refinement Module (TPRM) and a Semantic-Spatial Interaction Enhancement Module (SIEM). In TPRM, high-level features with rich semantic information are integrated through transformers as prior guidance, and then, it is sent to the refinement concurrency unit (RCU), and the accurately positioned feature area is obtained through a progressive refinement strategy. In SIEM, we perform feature interaction to localized-accurate semantic features and low-level features to obtain rich fine-grained clues and increase the symbolic power of boundary features. Extensive experiments on four widely used benchmark datasets (i.e., CAMO, CHAMELEON, COD10K, and NC4K) demonstrate that our TPRNet is an effective COD model and outperforms state-of-the-art models. The code is available https://github.com/zhangqiao970914/TPRNet. Camouflaged object detection (COD) is a challenging task which aims to detect objects similar to the surrounding environment. In this paper, we propose a transformer-induced progressive refinement network ( TPRNet ) to solve challenging COD tasks. Specifically, our network includes a Transformer-induced Progressive Refinement Module (TPRM) and a Semantic-Spatial Interaction Enhancement Module (SIEM). In TPRM, high-level features with rich semantic information are integrated through transformers as prior guidance, and then, it is sent to the refinement concurrency unit (RCU), and the accurately positioned feature area is obtained through a progressive refinement strategy. In SIEM, we perform feature interaction to localized-accurate semantic features and low-level features to obtain rich fine-grained clues and increase the symbolic power of boundary features. Extensive experiments on four widely used benchmark datasets (i.e., CAMO, CHAMELEON, COD10K, and NC4K) demonstrate that our TPRNet is an effective COD model and outperforms state-of-the-art models. The code is available https://github.com/zhangqiao970914/TPRNet . |
| Author | Ge, Yanliang Bi, Hongbo Zhang, Qiao Zhang, Cong |
| Author_xml | – sequence: 1 givenname: Qiao surname: Zhang fullname: Zhang, Qiao organization: School of electrical information engineering, Northeast Petroleum University – sequence: 2 givenname: Yanliang surname: Ge fullname: Ge, Yanliang organization: School of electrical information engineering, Northeast Petroleum University – sequence: 3 givenname: Cong surname: Zhang fullname: Zhang, Cong email: congzhang98@126.com organization: School of electrical information engineering, Northeast Petroleum University – sequence: 4 givenname: Hongbo orcidid: 0000-0003-2442-330X surname: Bi fullname: Bi, Hongbo email: bhbdq@126.com organization: School of electrical information engineering, Northeast Petroleum University |
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| Snippet | Camouflaged object detection (COD) is a challenging task which aims to detect objects similar to the surrounding environment. In this paper, we propose a... |
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| SubjectTerms | Artificial Intelligence Computer Graphics Computer Science Deep learning Image Processing and Computer Vision Methods Mining Modules Neural networks Object recognition Original Article Semantics Transformers |
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| Title | TPRNet: camouflaged object detection via transformer-induced progressive refinement network |
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