Guided multi-scale refinement network for camouflaged object detection
Abstract The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xu, Xiuqi [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 82(2022), 4 vom: 30. Juli, Seite 5785-5801 |
|---|---|
| Übergeordnetes Werk: |
volume:82 ; year:2022 ; number:4 ; day:30 ; month:07 ; pages:5785-5801 |
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| DOI / URN: |
10.1007/s11042-022-13274-4 |
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OLC2133557881 |
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| 520 | |a Abstract The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications. | ||
| 650 | 4 | |a Camouflaged object detection | |
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| 650 | 4 | |a Guided multi-scale refinement | |
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| 700 | 1 | |a Lv, Xiao |4 aut | |
| 700 | 1 | |a Wang, Jian |4 aut | |
| 700 | 1 | |a Hu, Xuelong |4 aut | |
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10.1007/s11042-022-13274-4 doi (DE-627)OLC2133557881 (DE-He213)s11042-022-13274-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Xu, Xiuqi verfasserin aut Guided multi-scale refinement network for camouflaged object detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications. Camouflaged object detection Multi-scale global perception Guided multi-scale refinement Chen, Shuhan (orcid)0000-0002-0094-5157 aut Lv, Xiao aut Wang, Jian aut Hu, Xuelong aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 4 vom: 30. Juli, Seite 5785-5801 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:4 day:30 month:07 pages:5785-5801 https://doi.org/10.1007/s11042-022-13274-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 4 30 07 5785-5801 |
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10.1007/s11042-022-13274-4 doi (DE-627)OLC2133557881 (DE-He213)s11042-022-13274-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Xu, Xiuqi verfasserin aut Guided multi-scale refinement network for camouflaged object detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications. Camouflaged object detection Multi-scale global perception Guided multi-scale refinement Chen, Shuhan (orcid)0000-0002-0094-5157 aut Lv, Xiao aut Wang, Jian aut Hu, Xuelong aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 4 vom: 30. Juli, Seite 5785-5801 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:4 day:30 month:07 pages:5785-5801 https://doi.org/10.1007/s11042-022-13274-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 4 30 07 5785-5801 |
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10.1007/s11042-022-13274-4 doi (DE-627)OLC2133557881 (DE-He213)s11042-022-13274-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Xu, Xiuqi verfasserin aut Guided multi-scale refinement network for camouflaged object detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications. Camouflaged object detection Multi-scale global perception Guided multi-scale refinement Chen, Shuhan (orcid)0000-0002-0094-5157 aut Lv, Xiao aut Wang, Jian aut Hu, Xuelong aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 4 vom: 30. Juli, Seite 5785-5801 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:4 day:30 month:07 pages:5785-5801 https://doi.org/10.1007/s11042-022-13274-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 4 30 07 5785-5801 |
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10.1007/s11042-022-13274-4 doi (DE-627)OLC2133557881 (DE-He213)s11042-022-13274-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Xu, Xiuqi verfasserin aut Guided multi-scale refinement network for camouflaged object detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications. Camouflaged object detection Multi-scale global perception Guided multi-scale refinement Chen, Shuhan (orcid)0000-0002-0094-5157 aut Lv, Xiao aut Wang, Jian aut Hu, Xuelong aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 4 vom: 30. Juli, Seite 5785-5801 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:4 day:30 month:07 pages:5785-5801 https://doi.org/10.1007/s11042-022-13274-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 4 30 07 5785-5801 |
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10.1007/s11042-022-13274-4 doi (DE-627)OLC2133557881 (DE-He213)s11042-022-13274-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Xu, Xiuqi verfasserin aut Guided multi-scale refinement network for camouflaged object detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications. Camouflaged object detection Multi-scale global perception Guided multi-scale refinement Chen, Shuhan (orcid)0000-0002-0094-5157 aut Lv, Xiao aut Wang, Jian aut Hu, Xuelong aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 4 vom: 30. Juli, Seite 5785-5801 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:4 day:30 month:07 pages:5785-5801 https://doi.org/10.1007/s11042-022-13274-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 4 30 07 5785-5801 |
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Guided multi-scale refinement network for camouflaged object detection |
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Abstract The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract The purpose of camouflaged object detection (COD) is to identify the hidden camouflaged object in an input image. Compared with other binary segmentation tasks like salient object detection, COD needs to deal with more complex scenes, such as low contrast, similar foreground and background. In this work, we proposed a novel guided multi-scale refinement network for COD. Specifically, we first design a global perception module for coarse localization by stacking multi-scale residual block on the top of the backbone in a recurrent manner. Then, we propose the guided multi-scale refinement module to refine such initial prediction progressively, which is combined with multi-level side-output features in a prediction-to-feature fusion strategy. By plugging into side-output features for multi-scale guidance, the missing object parts and false detection can be well remedied. Experimental results show that our proposed network can more accurately locate the camouflaged object and salient object with sharpened details than existing state-of-the-art approaches. In addition, our model is also very efficient and compact, which enables potential real-world applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| title_short |
Guided multi-scale refinement network for camouflaged object detection |
| url |
https://doi.org/10.1007/s11042-022-13274-4 |
| remote_bool |
false |
| author2 |
Chen, Shuhan Lv, Xiao Wang, Jian Hu, Xuelong |
| author2Str |
Chen, Shuhan Lv, Xiao Wang, Jian Hu, Xuelong |
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189064145 |
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| doi_str |
10.1007/s11042-022-13274-4 |
| up_date |
2024-07-03T20:11:12.301Z |
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