CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework
In this paper, we propose a framework for evaluation of co-salient object detection algorithms along with two novel CoSOD methods. The processing pipeline of this framework is based on the CoEGNet algorithm, where the saliency detection part can be easily replaced by any saliency detector to be eval...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9853520/ |
| _version_ | 1798037127979597824 |
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| author | Jakub Korczakowski Grzegorz Sarwas Witold Czajewski |
| author_facet | Jakub Korczakowski Grzegorz Sarwas Witold Czajewski |
| author_sort | Jakub Korczakowski |
| collection | DOAJ |
| description | In this paper, we propose a framework for evaluation of co-salient object detection algorithms along with two novel CoSOD methods. The processing pipeline of this framework is based on the CoEGNet algorithm, where the saliency detection part can be easily replaced by any saliency detector to be evaluated. By leveraging the proposed framework, we developed two new algorithms: one based on U2Net and the other based on the label decoupling framework (LDF). They are called in this paper CoU2Net and CoLDF, respectively. The proposed solutions were tested on three datasets: CoCA, CoSal2015, and CoSOD3k, and compared with some of the best algorithms in co-salient object detection: GICD and CoEGNet. The advantages and disadvantages of the proposed methods are highlighted and discussed. As a generalization of the aforementioned methods, we also propose a framework called Sal.Co. It is a modification of the CoEGNet method and it works on a saliency mask obtained from a saliency detector and attempts to indicate salient objects coexisting in a group of images. Both CoLDF and CoU2Net achieved better results than CoEGNet on the CoCA dataset. On the CoSal2015 and CoSOD3k datasets, they performed similarly to state-of-the-art methods, while maintaining a highly customizable structure. The source code can be found at: <uri>https://github.com/jakubkorczakowski/cosal_sal_testing</uri>. |
| first_indexed | 2024-04-11T21:22:22Z |
| format | Article |
| id | doaj.art-8ddca74f31844454bd8509f8f93f8053 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T21:22:22Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8ddca74f31844454bd8509f8f93f80532022-12-22T04:02:34ZengIEEEIEEE Access2169-35362022-01-0110849898500110.1109/ACCESS.2022.31977529853520CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection FrameworkJakub Korczakowski0Grzegorz Sarwas1https://orcid.org/0000-0003-4113-2387Witold Czajewski2https://orcid.org/0000-0003-2118-8965Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, PolandFaculty of Electrical Engineering, Warsaw University of Technology, Warsaw, PolandFaculty of Electrical Engineering, Warsaw University of Technology, Warsaw, PolandIn this paper, we propose a framework for evaluation of co-salient object detection algorithms along with two novel CoSOD methods. The processing pipeline of this framework is based on the CoEGNet algorithm, where the saliency detection part can be easily replaced by any saliency detector to be evaluated. By leveraging the proposed framework, we developed two new algorithms: one based on U2Net and the other based on the label decoupling framework (LDF). They are called in this paper CoU2Net and CoLDF, respectively. The proposed solutions were tested on three datasets: CoCA, CoSal2015, and CoSOD3k, and compared with some of the best algorithms in co-salient object detection: GICD and CoEGNet. The advantages and disadvantages of the proposed methods are highlighted and discussed. As a generalization of the aforementioned methods, we also propose a framework called Sal.Co. It is a modification of the CoEGNet method and it works on a saliency mask obtained from a saliency detector and attempts to indicate salient objects coexisting in a group of images. Both CoLDF and CoU2Net achieved better results than CoEGNet on the CoCA dataset. On the CoSal2015 and CoSOD3k datasets, they performed similarly to state-of-the-art methods, while maintaining a highly customizable structure. The source code can be found at: <uri>https://github.com/jakubkorczakowski/cosal_sal_testing</uri>.https://ieeexplore.ieee.org/document/9853520/Deep learningimage analysisobject detectionpattern recognition |
| spellingShingle | Jakub Korczakowski Grzegorz Sarwas Witold Czajewski CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework IEEE Access Deep learning image analysis object detection pattern recognition |
| title | CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework |
| title_full | CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework |
| title_fullStr | CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework |
| title_full_unstemmed | CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework |
| title_short | CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection Framework |
| title_sort | cou2net and coldf two novel methods built on basis of double branch co salient object detection framework |
| topic | Deep learning image analysis object detection pattern recognition |
| url | https://ieeexplore.ieee.org/document/9853520/ |
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