Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2024

Abstract

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.

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI-24), Jeju, Korea, 2024 August 3-9

First Page

1

Last Page

9

Publisher

IJCAI

City or Country

California

Copyright Owner and License

Authors

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