Feature-attention module for context-aware image-to-image translation

Abstract In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Bai, Jing [verfasserIn] Chen, Ran [verfasserIn] Liu, Min [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Feature-attention Unsupervised image-to-image translation Context-aware

Übergeordnetes Werk:	Enthalten in: The visual computer - Berlin : Springer, 1985, 36(2020), 10-12 vom: 23. Sept., Seite 2145-2159
Übergeordnetes Werk:	volume:36 ; year:2020 ; number:10-12 ; day:23 ; month:09 ; pages:2145-2159

Links:	Volltext

DOI / URN:	10.1007/s00371-020-01943-0

Katalog-ID:	SPR041140974

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520			\|a Abstract In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. We also show that the computation complexity of the proposed module is linear to the image size; moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. The source code and trained models are available at https://github.com/gaoyuainshuyi/fa.
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author_variant	j b jb r c rc m l ml
matchkey_str	article:14322315:2020----::etratninoueocnetwriaeo
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allfields	10.1007/s00371-020-01943-0 doi (DE-627)SPR041140974 (SPR)s00371-020-01943-0-e DE-627 ger DE-627 rakwb eng 004 ASE 54.73 bkl Bai, Jing verfasserin aut Feature-attention module for context-aware image-to-image translation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. We also show that the computation complexity of the proposed module is linear to the image size; moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. The source code and trained models are available at https://github.com/gaoyuainshuyi/fa. Feature-attention (dpeaa)DE-He213 Unsupervised image-to-image translation (dpeaa)DE-He213 Context-aware (dpeaa)DE-He213 Chen, Ran verfasserin aut Liu, Min verfasserin aut Enthalten in The visual computer Berlin : Springer, 1985 36(2020), 10-12 vom: 23. Sept., Seite 2145-2159 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:36 year:2020 number:10-12 day:23 month:09 pages:2145-2159 https://dx.doi.org/10.1007/s00371-020-01943-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 ASE AR 36 2020 10-12 23 09 2145-2159
spelling	10.1007/s00371-020-01943-0 doi (DE-627)SPR041140974 (SPR)s00371-020-01943-0-e DE-627 ger DE-627 rakwb eng 004 ASE 54.73 bkl Bai, Jing verfasserin aut Feature-attention module for context-aware image-to-image translation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. We also show that the computation complexity of the proposed module is linear to the image size; moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. The source code and trained models are available at https://github.com/gaoyuainshuyi/fa. Feature-attention (dpeaa)DE-He213 Unsupervised image-to-image translation (dpeaa)DE-He213 Context-aware (dpeaa)DE-He213 Chen, Ran verfasserin aut Liu, Min verfasserin aut Enthalten in The visual computer Berlin : Springer, 1985 36(2020), 10-12 vom: 23. Sept., Seite 2145-2159 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:36 year:2020 number:10-12 day:23 month:09 pages:2145-2159 https://dx.doi.org/10.1007/s00371-020-01943-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 ASE AR 36 2020 10-12 23 09 2145-2159
allfields_unstemmed	10.1007/s00371-020-01943-0 doi (DE-627)SPR041140974 (SPR)s00371-020-01943-0-e DE-627 ger DE-627 rakwb eng 004 ASE 54.73 bkl Bai, Jing verfasserin aut Feature-attention module for context-aware image-to-image translation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. We also show that the computation complexity of the proposed module is linear to the image size; moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. The source code and trained models are available at https://github.com/gaoyuainshuyi/fa. Feature-attention (dpeaa)DE-He213 Unsupervised image-to-image translation (dpeaa)DE-He213 Context-aware (dpeaa)DE-He213 Chen, Ran verfasserin aut Liu, Min verfasserin aut Enthalten in The visual computer Berlin : Springer, 1985 36(2020), 10-12 vom: 23. Sept., Seite 2145-2159 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:36 year:2020 number:10-12 day:23 month:09 pages:2145-2159 https://dx.doi.org/10.1007/s00371-020-01943-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 ASE AR 36 2020 10-12 23 09 2145-2159
allfieldsGer	10.1007/s00371-020-01943-0 doi (DE-627)SPR041140974 (SPR)s00371-020-01943-0-e DE-627 ger DE-627 rakwb eng 004 ASE 54.73 bkl Bai, Jing verfasserin aut Feature-attention module for context-aware image-to-image translation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. We also show that the computation complexity of the proposed module is linear to the image size; moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. The source code and trained models are available at https://github.com/gaoyuainshuyi/fa. Feature-attention (dpeaa)DE-He213 Unsupervised image-to-image translation (dpeaa)DE-He213 Context-aware (dpeaa)DE-He213 Chen, Ran verfasserin aut Liu, Min verfasserin aut Enthalten in The visual computer Berlin : Springer, 1985 36(2020), 10-12 vom: 23. Sept., Seite 2145-2159 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:36 year:2020 number:10-12 day:23 month:09 pages:2145-2159 https://dx.doi.org/10.1007/s00371-020-01943-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 ASE AR 36 2020 10-12 23 09 2145-2159
allfieldsSound	10.1007/s00371-020-01943-0 doi (DE-627)SPR041140974 (SPR)s00371-020-01943-0-e DE-627 ger DE-627 rakwb eng 004 ASE 54.73 bkl Bai, Jing verfasserin aut Feature-attention module for context-aware image-to-image translation 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. We also show that the computation complexity of the proposed module is linear to the image size; moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. The source code and trained models are available at https://github.com/gaoyuainshuyi/fa. Feature-attention (dpeaa)DE-He213 Unsupervised image-to-image translation (dpeaa)DE-He213 Context-aware (dpeaa)DE-He213 Chen, Ran verfasserin aut Liu, Min verfasserin aut Enthalten in The visual computer Berlin : Springer, 1985 36(2020), 10-12 vom: 23. Sept., Seite 2145-2159 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:36 year:2020 number:10-12 day:23 month:09 pages:2145-2159 https://dx.doi.org/10.1007/s00371-020-01943-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 ASE AR 36 2020 10-12 23 09 2145-2159
language	English
source	Enthalten in The visual computer 36(2020), 10-12 vom: 23. Sept., Seite 2145-2159 volume:36 year:2020 number:10-12 day:23 month:09 pages:2145-2159
sourceStr	Enthalten in The visual computer 36(2020), 10-12 vom: 23. Sept., Seite 2145-2159 volume:36 year:2020 number:10-12 day:23 month:09 pages:2145-2159
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Feature-attention Unsupervised image-to-image translation Context-aware
dewey-raw	004
isfreeaccess_bool	false
container_title	The visual computer
authorswithroles_txt_mv	Bai, Jing @@aut@@ Chen, Ran @@aut@@ Liu, Min @@aut@@
publishDateDaySort_date	2020-09-23T00:00:00Z
hierarchy_top_id	254910734
dewey-sort	14
id	SPR041140974
language_de	englisch
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However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. We also show that the computation complexity of the proposed module is linear to the image size; moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. 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author	Bai, Jing
spellingShingle	Bai, Jing ddc 004 bkl 54.73 misc Feature-attention misc Unsupervised image-to-image translation misc Context-aware Feature-attention module for context-aware image-to-image translation
authorStr	Bai, Jing
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topic_title	004 ASE 54.73 bkl Feature-attention module for context-aware image-to-image translation Feature-attention (dpeaa)DE-He213 Unsupervised image-to-image translation (dpeaa)DE-He213 Context-aware (dpeaa)DE-He213
topic	ddc 004 bkl 54.73 misc Feature-attention misc Unsupervised image-to-image translation misc Context-aware
topic_unstemmed	ddc 004 bkl 54.73 misc Feature-attention misc Unsupervised image-to-image translation misc Context-aware
topic_browse	ddc 004 bkl 54.73 misc Feature-attention misc Unsupervised image-to-image translation misc Context-aware
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title	Feature-attention module for context-aware image-to-image translation
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title_full	Feature-attention module for context-aware image-to-image translation
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author_browse	Bai, Jing Chen, Ran Liu, Min
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title_sort	feature-attention module for context-aware image-to-image translation
title_auth	Feature-attention module for context-aware image-to-image translation
abstract	Abstract In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. We also show that the computation complexity of the proposed module is linear to the image size; moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. The source code and trained models are available at https://github.com/gaoyuainshuyi/fa.
abstractGer	Abstract In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. We also show that the computation complexity of the proposed module is linear to the image size; moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. The source code and trained models are available at https://github.com/gaoyuainshuyi/fa.
abstract_unstemmed	Abstract In a summer2winter image-to-image translation, trees should be transformed from green to gray, but the colors of houses or girls should not be changed. However, current unsupervised one-to-one image translation techniques failed to focus the translation on individual objects. To tackle this issue, we propose a novel feature-attention module for capturing the mutual influences of various features, so as to automatically attend only to specific scene objects in unsupervised image-to-image translation. The proposed module can be integrated into different image translation networks and improve their context-aware translation ability. The qualitative and quantitative experiments on horse2zebra, apple2orange and summer2winter datasets based on DualGAN, CycleGAN and UNIT demonstrate a significant improvement in our proposed module over the state-of-the-art methods. In addition, the experiments on apple2orange dataset based on MUNIT and DRIT further indicate the effectiveness of FA module in multimodal translation tasks. We also show that the computation complexity of the proposed module is linear to the image size; moreover, the experiments on the day2night dataset prove that the proposed module is insensitive to the growth of image resolution. The source code and trained models are available at https://github.com/gaoyuainshuyi/fa.
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container_issue	10-12
title_short	Feature-attention module for context-aware image-to-image translation
url	https://dx.doi.org/10.1007/s00371-020-01943-0
remote_bool	true
author2	Chen, Ran Liu, Min
author2Str	Chen, Ran Liu, Min
ppnlink	254910734
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s00371-020-01943-0
up_date	2024-07-03T20:28:33.979Z
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