{"type":"ScholarlyArticle","title":"CausVSR: Causality Inspired Visual Sentiment Recognition","authors":[{"name":"Xinyue Zhang","slug":"xinyue-zhang","given":"Xinyue","family":"Zhang","affiliation":"Shanghai Institute of Artificial Intelligence for Education , East China; MoE Engineering Research Center of SW/HW Co-design Technology and Application , East China Normal University"},{"name":"Zhaoxia Wang","slug":"zhaoxia-wang","given":"Zhaoxia","family":"Wang","affiliation":"School of Computing and Information Systems , Singapore Management University"},{"name":"Hailing Wang","slug":"hailing-wang","given":"Hailing","family":"Wang","affiliation":"MoE Engineering Research Center of SW/HW Co-design Technology and Application , East China Normal University; Shanghai Key Laboratory of Trustworthy Computing , East China Normal University"},{"name":"Jing Xiang","slug":"jing-xiang","given":"Jing","family":"Xiang","affiliation":"MoE Engineering Research Center of SW/HW Co-design Technology and Application , East China Normal University; Shanghai Key Laboratory of Trustworthy Computing , East China Normal University"},{"name":"Chunwei Wu","slug":"chunwei-wu","given":"Chunwei","family":"Wu","affiliation":"MoE Engineering Research Center of SW/HW Co-design Technology and Application , East China Normal University; Shanghai Key Laboratory of Trustworthy Computing , East China Normal University; Normal University"},{"name":"Guitao Cao","slug":"guitao-cao","given":"Guitao","family":"Cao","affiliation":"MoE Engineering Research Center of SW/HW Co-design Technology and Application , East China Normal University; Shanghai Key Laboratory of Trustworthy Computing , East China Normal University"}],"abstract":"Visual Sentiment Recognition (VSR) is an evolving field that aims to detect emotional tendencies within visual content. Despite its growing significance, detecting emotions depicted in visual content, such as images, faces challenges, notably the emergence of misleading or spurious correlations of the contextual information. In response to these challenges, we propose a causality inspired VSR approach, called CausVSR. CausVSR is rooted in the fundamental principles of Emotional Causality theory, 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 a structural causal model, intricately designed to encapsulate the dynamic causal interplay between visual content and their corresponding pseudo sentiment regions. This strategic approach allows for a deep exploration of contextual information, elevating the accuracy of emotional inference. Additionally, CausVSR utilizes a global category elicitation module, strategically employed to execute front-door adjustment techniques, effectively detecting and handling spurious correlations. Experiments, conducted on four widely-used datasets, demonstrate CausVSR's superiority in enhancing emotion perception within VSR, surpassing existing methods.","doi":"10.24963/ijcai.2024/354","url":"https://ijcai.org/proceedings/2024/354","pdf":"https://ijcai.org/proceedings/2024/0354.pdf","markdown":"https://ijcai.org/proceedings/2024/354.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/354","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Humans and AI","track":"Main Track","number":354,"pages":{"first":3196,"last":3204},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Machine learning for vision","Humans and AI: HAI: Cognitive modeling","Machine Learning: ML: Deep learning architectures"],"videos":[],"references":[{"position":1,"raw":"[Amrani et al., 2022] Elad Amrani, Leonid Karlinsky, and Alex Bronstein. 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