Affective word embedding in affective explanation generation for fine art paintings

Fine art paintings take an important place in human history and are one of the fundamental components of human culture. Even though deep learning in fine art paintings attracts increasing attention from researchers, few works focus on understanding the interplay between the visual content, its trigg...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yan, Jianhao [verfasserIn] Wang, Wenmin [verfasserIn] Yu, Cheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Explanation generation Affective word embedding Fine art painting

Übergeordnetes Werk:	Enthalten in: Pattern recognition letters - Amsterdam [u.a.] : Elsevier, 1982, 161, Seite 24-29
Übergeordnetes Werk:	volume:161 ; pages:24-29

DOI / URN:	10.1016/j.patrec.2022.07.009

Katalog-ID:	ELV008386374

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allfields	10.1016/j.patrec.2022.07.009 doi (DE-627)ELV008386374 (ELSEVIER)S0167-8655(22)00222-7 DE-627 ger DE-627 rda eng 004 DE-600 54.74 bkl Yan, Jianhao verfasserin (orcid)0000-0003-2464-9069 aut Affective word embedding in affective explanation generation for fine art paintings 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fine art paintings take an important place in human history and are one of the fundamental components of human culture. Even though deep learning in fine art paintings attracts increasing attention from researchers, few works focus on understanding the interplay between the visual content, its triggered affect, and language aiming to explain that affect. Most visual captioning models can only deal with tasks of generating captions describing objective affairs but lack the capabilities of generating affective explanations. In this paper, we introduce the use of the VAD (Valence, Arousal, and Dominance) dictionary in our model and propose a gated concatenation mechanism to construct word affective embedding. Corporating with the use of the affective loss function, our model outperforms the state-of-the-art in automatic evaluation metrics and subjective evaluations. Explanation generation Affective word embedding Fine art painting Wang, Wenmin verfasserin (orcid)0000-0003-2664-4413 aut Yu, Cheng verfasserin aut Enthalten in Pattern recognition letters Amsterdam [u.a.] : Elsevier, 1982 161, Seite 24-29 Online-Ressource (DE-627)265784123 (DE-600)1466342-9 (DE-576)074891006 0167-8655 nnns volume:161 pages:24-29 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 161 24-29
spelling	10.1016/j.patrec.2022.07.009 doi (DE-627)ELV008386374 (ELSEVIER)S0167-8655(22)00222-7 DE-627 ger DE-627 rda eng 004 DE-600 54.74 bkl Yan, Jianhao verfasserin (orcid)0000-0003-2464-9069 aut Affective word embedding in affective explanation generation for fine art paintings 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fine art paintings take an important place in human history and are one of the fundamental components of human culture. Even though deep learning in fine art paintings attracts increasing attention from researchers, few works focus on understanding the interplay between the visual content, its triggered affect, and language aiming to explain that affect. Most visual captioning models can only deal with tasks of generating captions describing objective affairs but lack the capabilities of generating affective explanations. In this paper, we introduce the use of the VAD (Valence, Arousal, and Dominance) dictionary in our model and propose a gated concatenation mechanism to construct word affective embedding. Corporating with the use of the affective loss function, our model outperforms the state-of-the-art in automatic evaluation metrics and subjective evaluations. Explanation generation Affective word embedding Fine art painting Wang, Wenmin verfasserin (orcid)0000-0003-2664-4413 aut Yu, Cheng verfasserin aut Enthalten in Pattern recognition letters Amsterdam [u.a.] : Elsevier, 1982 161, Seite 24-29 Online-Ressource (DE-627)265784123 (DE-600)1466342-9 (DE-576)074891006 0167-8655 nnns volume:161 pages:24-29 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 161 24-29
allfields_unstemmed	10.1016/j.patrec.2022.07.009 doi (DE-627)ELV008386374 (ELSEVIER)S0167-8655(22)00222-7 DE-627 ger DE-627 rda eng 004 DE-600 54.74 bkl Yan, Jianhao verfasserin (orcid)0000-0003-2464-9069 aut Affective word embedding in affective explanation generation for fine art paintings 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fine art paintings take an important place in human history and are one of the fundamental components of human culture. Even though deep learning in fine art paintings attracts increasing attention from researchers, few works focus on understanding the interplay between the visual content, its triggered affect, and language aiming to explain that affect. Most visual captioning models can only deal with tasks of generating captions describing objective affairs but lack the capabilities of generating affective explanations. In this paper, we introduce the use of the VAD (Valence, Arousal, and Dominance) dictionary in our model and propose a gated concatenation mechanism to construct word affective embedding. Corporating with the use of the affective loss function, our model outperforms the state-of-the-art in automatic evaluation metrics and subjective evaluations. Explanation generation Affective word embedding Fine art painting Wang, Wenmin verfasserin (orcid)0000-0003-2664-4413 aut Yu, Cheng verfasserin aut Enthalten in Pattern recognition letters Amsterdam [u.a.] : Elsevier, 1982 161, Seite 24-29 Online-Ressource (DE-627)265784123 (DE-600)1466342-9 (DE-576)074891006 0167-8655 nnns volume:161 pages:24-29 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 161 24-29
allfieldsGer	10.1016/j.patrec.2022.07.009 doi (DE-627)ELV008386374 (ELSEVIER)S0167-8655(22)00222-7 DE-627 ger DE-627 rda eng 004 DE-600 54.74 bkl Yan, Jianhao verfasserin (orcid)0000-0003-2464-9069 aut Affective word embedding in affective explanation generation for fine art paintings 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fine art paintings take an important place in human history and are one of the fundamental components of human culture. Even though deep learning in fine art paintings attracts increasing attention from researchers, few works focus on understanding the interplay between the visual content, its triggered affect, and language aiming to explain that affect. Most visual captioning models can only deal with tasks of generating captions describing objective affairs but lack the capabilities of generating affective explanations. In this paper, we introduce the use of the VAD (Valence, Arousal, and Dominance) dictionary in our model and propose a gated concatenation mechanism to construct word affective embedding. Corporating with the use of the affective loss function, our model outperforms the state-of-the-art in automatic evaluation metrics and subjective evaluations. Explanation generation Affective word embedding Fine art painting Wang, Wenmin verfasserin (orcid)0000-0003-2664-4413 aut Yu, Cheng verfasserin aut Enthalten in Pattern recognition letters Amsterdam [u.a.] : Elsevier, 1982 161, Seite 24-29 Online-Ressource (DE-627)265784123 (DE-600)1466342-9 (DE-576)074891006 0167-8655 nnns volume:161 pages:24-29 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 161 24-29
allfieldsSound	10.1016/j.patrec.2022.07.009 doi (DE-627)ELV008386374 (ELSEVIER)S0167-8655(22)00222-7 DE-627 ger DE-627 rda eng 004 DE-600 54.74 bkl Yan, Jianhao verfasserin (orcid)0000-0003-2464-9069 aut Affective word embedding in affective explanation generation for fine art paintings 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fine art paintings take an important place in human history and are one of the fundamental components of human culture. Even though deep learning in fine art paintings attracts increasing attention from researchers, few works focus on understanding the interplay between the visual content, its triggered affect, and language aiming to explain that affect. Most visual captioning models can only deal with tasks of generating captions describing objective affairs but lack the capabilities of generating affective explanations. In this paper, we introduce the use of the VAD (Valence, Arousal, and Dominance) dictionary in our model and propose a gated concatenation mechanism to construct word affective embedding. Corporating with the use of the affective loss function, our model outperforms the state-of-the-art in automatic evaluation metrics and subjective evaluations. Explanation generation Affective word embedding Fine art painting Wang, Wenmin verfasserin (orcid)0000-0003-2664-4413 aut Yu, Cheng verfasserin aut Enthalten in Pattern recognition letters Amsterdam [u.a.] : Elsevier, 1982 161, Seite 24-29 Online-Ressource (DE-627)265784123 (DE-600)1466342-9 (DE-576)074891006 0167-8655 nnns volume:161 pages:24-29 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.74 Maschinelles Sehen AR 161 24-29
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topic_title	004 DE-600 54.74 bkl Affective word embedding in affective explanation generation for fine art paintings Explanation generation Affective word embedding Fine art painting
topic	ddc 004 bkl 54.74 misc Explanation generation misc Affective word embedding misc Fine art painting
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title	Affective word embedding in affective explanation generation for fine art paintings
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title_sort	affective word embedding in affective explanation generation for fine art paintings
title_auth	Affective word embedding in affective explanation generation for fine art paintings
abstract	Fine art paintings take an important place in human history and are one of the fundamental components of human culture. Even though deep learning in fine art paintings attracts increasing attention from researchers, few works focus on understanding the interplay between the visual content, its triggered affect, and language aiming to explain that affect. Most visual captioning models can only deal with tasks of generating captions describing objective affairs but lack the capabilities of generating affective explanations. In this paper, we introduce the use of the VAD (Valence, Arousal, and Dominance) dictionary in our model and propose a gated concatenation mechanism to construct word affective embedding. Corporating with the use of the affective loss function, our model outperforms the state-of-the-art in automatic evaluation metrics and subjective evaluations.
abstractGer	Fine art paintings take an important place in human history and are one of the fundamental components of human culture. Even though deep learning in fine art paintings attracts increasing attention from researchers, few works focus on understanding the interplay between the visual content, its triggered affect, and language aiming to explain that affect. Most visual captioning models can only deal with tasks of generating captions describing objective affairs but lack the capabilities of generating affective explanations. In this paper, we introduce the use of the VAD (Valence, Arousal, and Dominance) dictionary in our model and propose a gated concatenation mechanism to construct word affective embedding. Corporating with the use of the affective loss function, our model outperforms the state-of-the-art in automatic evaluation metrics and subjective evaluations.
abstract_unstemmed	Fine art paintings take an important place in human history and are one of the fundamental components of human culture. Even though deep learning in fine art paintings attracts increasing attention from researchers, few works focus on understanding the interplay between the visual content, its triggered affect, and language aiming to explain that affect. Most visual captioning models can only deal with tasks of generating captions describing objective affairs but lack the capabilities of generating affective explanations. In this paper, we introduce the use of the VAD (Valence, Arousal, and Dominance) dictionary in our model and propose a gated concatenation mechanism to construct word affective embedding. Corporating with the use of the affective loss function, our model outperforms the state-of-the-art in automatic evaluation metrics and subjective evaluations.
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title_short	Affective word embedding in affective explanation generation for fine art paintings
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up_date	2024-07-06T19:31:34.018Z
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Even though deep learning in fine art paintings attracts increasing attention from researchers, few works focus on understanding the interplay between the visual content, its triggered affect, and language aiming to explain that affect. Most visual captioning models can only deal with tasks of generating captions describing objective affairs but lack the capabilities of generating affective explanations. In this paper, we introduce the use of the VAD (Valence, Arousal, and Dominance) dictionary in our model and propose a gated concatenation mechanism to construct word affective embedding. Corporating with the use of the affective loss function, our model outperforms the state-of-the-art in automatic evaluation metrics and subjective evaluations.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Explanation generation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Affective word embedding</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fine art painting</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Wenmin</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-2664-4413</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yu, Cheng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pattern recognition 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Affective word embedding in affective explanation generation for fine art paintings

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