Group attention retention network for co-salient object detection
The co-salient object detection (Co-SOD) aims to discover common, salient objects from a group of images. With the development of convolutional neural networks, the performance of Co-SOD methods has been significantly improved. However, some models cannot construct collaborative relationships across...
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| Published in | Machine vision and applications Vol. 34; no. 6; p. 107 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2023
Springer Nature B.V |
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| Abstract | The co-salient object detection (Co-SOD) aims to discover common, salient objects from a group of images. With the development of convolutional neural networks, the performance of Co-SOD methods has been significantly improved. However, some models cannot construct collaborative relationships across images optimally and lack effective retention of collaborative features in the top-down decoding process. In this paper, we propose a novel group attention retention network (GARNet), which captures excellent collaborative features and retains them. First, a group attention module is designed to construct the inter-image relationships. Second, an attention retention module and a spatial attention module are designed to retain inter-image relationships for protecting them from being diluted and filter out the cluttered context during feature fusion, respectively. Finally, considering the intra-group consistency and inter-group separability of images, an embedding loss is additionally designed to discriminate between real collaborative objects and distracting objects. The experiments on four datasets (iCoSeg, CoSal2015, CoSoD3k, and CoCA) show that our GARNet outperforms previous state-of-the-art methods. The source code is available at
https://github.com/TJUMMG/GARNet
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| AbstractList | The co-salient object detection (Co-SOD) aims to discover common, salient objects from a group of images. With the development of convolutional neural networks, the performance of Co-SOD methods has been significantly improved. However, some models cannot construct collaborative relationships across images optimally and lack effective retention of collaborative features in the top-down decoding process. In this paper, we propose a novel group attention retention network (GARNet), which captures excellent collaborative features and retains them. First, a group attention module is designed to construct the inter-image relationships. Second, an attention retention module and a spatial attention module are designed to retain inter-image relationships for protecting them from being diluted and filter out the cluttered context during feature fusion, respectively. Finally, considering the intra-group consistency and inter-group separability of images, an embedding loss is additionally designed to discriminate between real collaborative objects and distracting objects. The experiments on four datasets (iCoSeg, CoSal2015, CoSoD3k, and CoCA) show that our GARNet outperforms previous state-of-the-art methods. The source code is available at https://github.com/TJUMMG/GARNet. The co-salient object detection (Co-SOD) aims to discover common, salient objects from a group of images. With the development of convolutional neural networks, the performance of Co-SOD methods has been significantly improved. However, some models cannot construct collaborative relationships across images optimally and lack effective retention of collaborative features in the top-down decoding process. In this paper, we propose a novel group attention retention network (GARNet), which captures excellent collaborative features and retains them. First, a group attention module is designed to construct the inter-image relationships. Second, an attention retention module and a spatial attention module are designed to retain inter-image relationships for protecting them from being diluted and filter out the cluttered context during feature fusion, respectively. Finally, considering the intra-group consistency and inter-group separability of images, an embedding loss is additionally designed to discriminate between real collaborative objects and distracting objects. The experiments on four datasets (iCoSeg, CoSal2015, CoSoD3k, and CoCA) show that our GARNet outperforms previous state-of-the-art methods. The source code is available at https://github.com/TJUMMG/GARNet . |
| ArticleNumber | 107 |
| Author | Wang, Weikang Fan, Zhiwei Yu, Jiexiao Liu, Jing Yuan, Min Wang, Jiaxiang |
| Author_xml | – sequence: 1 givenname: Jing surname: Liu fullname: Liu, Jing organization: The School of Electrical and Information Engineering, Tianjin University, Key Laboratory of Artificial Intelligence, Ministry of Education – sequence: 2 givenname: Jiaxiang surname: Wang fullname: Wang, Jiaxiang organization: The School of Electrical and Information Engineering, Tianjin University – sequence: 3 givenname: Zhiwei surname: Fan fullname: Fan, Zhiwei organization: The School of Electrical and Information Engineering, Tianjin University – sequence: 4 givenname: Min surname: Yuan fullname: Yuan, Min organization: The School of Electrical and Information Engineering, Tianjin University – sequence: 5 givenname: Weikang surname: Wang fullname: Wang, Weikang email: wwk_19970307@tju.edu.cn organization: The School of Electrical and Information Engineering, Tianjin University – sequence: 6 givenname: Jiexiao surname: Yu fullname: Yu, Jiexiao organization: The School of Electrical and Information Engineering, Tianjin University |
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| Keywords | Vision transformer Attention retention Co-salient object detection Group attention |
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| Title | Group attention retention network for co-salient object detection |
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