CausVSR: Causality inspired visual sentiment recognition

Visual Sentiment Recognition (VSR) is an evolving field that aims to detect emotional tendencieswithin visual content. Despite its growing significance, detecting emotions depicted in visual content,such as images, faces challenges, notably the emergence of misleading or spurious correlationsof the...

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Main Authors: ZHANG, Xinyue, WANG, Zhaoxia, WANG, Hailing, XIANG, Jing, WU, Chunwei, CAO, Guitao
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9158
https://ink.library.smu.edu.sg/context/sis_research/article/10161/viewcontent/CausVSR_Causality_Inspired_Visual_Sentiment_Recognition_IJCAI.pdf
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spelling sg-smu-ink.sis_research-101612024-08-01T08:40:54Z CausVSR: Causality inspired visual sentiment recognition ZHANG, Xinyue WANG, Zhaoxia WANG, Hailing XIANG, Jing WU, Chunwei CAO, Guitao Visual Sentiment Recognition (VSR) is an evolving field that aims to detect emotional tendencieswithin visual content. Despite its growing significance, detecting emotions depicted in visual content,such as images, faces challenges, notably the emergence of misleading or spurious correlationsof the contextual information. In response to these challenges, we propose a causality inspired VSRapproach, called CausVSR. CausVSR is rooted in the fundamental principles of Emotional Causalitytheory, mimicking the human process from receiving emotional stimuli to deriving emotional states.CausVSR takes a deliberate stride toward conquering the VSR challenges. It harnesses the power of astructural causal model, intricately designed to encapsulate the dynamic causal interplay between visualcontent and their corresponding pseudo sentiment regions. This strategic approach allows for adeep exploration of contextual information, elevating the accuracy of emotional inference. Additionally,CausVSR utilizes a global category elicitation module, strategically employed to execute frontdooradjustment techniques, effectively detecting and handling spurious correlations. Experiments,conducted on four widely-used datasets, demonstrate CausVSR’s superiority in enhancing emotionperception within VSR, surpassing existing methods. 2024-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9158 https://ink.library.smu.edu.sg/context/sis_research/article/10161/viewcontent/CausVSR_Causality_Inspired_Visual_Sentiment_Recognition_IJCAI.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Artificial Intelligence and Robotics
Databases and Information Systems
spellingShingle Artificial Intelligence and Robotics
Databases and Information Systems
ZHANG, Xinyue
WANG, Zhaoxia
WANG, Hailing
XIANG, Jing
WU, Chunwei
CAO, Guitao
CausVSR: Causality inspired visual sentiment recognition
description Visual Sentiment Recognition (VSR) is an evolving field that aims to detect emotional tendencieswithin visual content. Despite its growing significance, detecting emotions depicted in visual content,such as images, faces challenges, notably the emergence of misleading or spurious correlationsof the contextual information. In response to these challenges, we propose a causality inspired VSRapproach, called CausVSR. CausVSR is rooted in the fundamental principles of Emotional Causalitytheory, mimicking the human process from receiving emotional stimuli to deriving emotional states.CausVSR takes a deliberate stride toward conquering the VSR challenges. It harnesses the power of astructural causal model, intricately designed to encapsulate the dynamic causal interplay between visualcontent and their corresponding pseudo sentiment regions. This strategic approach allows for adeep exploration of contextual information, elevating the accuracy of emotional inference. Additionally,CausVSR utilizes a global category elicitation module, strategically employed to execute frontdooradjustment techniques, effectively detecting and handling spurious correlations. Experiments,conducted on four widely-used datasets, demonstrate CausVSR’s superiority in enhancing emotionperception within VSR, surpassing existing methods.
format text
author ZHANG, Xinyue
WANG, Zhaoxia
WANG, Hailing
XIANG, Jing
WU, Chunwei
CAO, Guitao
author_facet ZHANG, Xinyue
WANG, Zhaoxia
WANG, Hailing
XIANG, Jing
WU, Chunwei
CAO, Guitao
author_sort ZHANG, Xinyue
title CausVSR: Causality inspired visual sentiment recognition
title_short CausVSR: Causality inspired visual sentiment recognition
title_full CausVSR: Causality inspired visual sentiment recognition
title_fullStr CausVSR: Causality inspired visual sentiment recognition
title_full_unstemmed CausVSR: Causality inspired visual sentiment recognition
title_sort causvsr: causality inspired visual sentiment recognition
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9158
https://ink.library.smu.edu.sg/context/sis_research/article/10161/viewcontent/CausVSR_Causality_Inspired_Visual_Sentiment_Recognition_IJCAI.pdf
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