ScholarWorks@성균관대학교

초록

Visual emotion analysis is a promising field that aims to predict emotional responses elicited by visual stimuli. While recent advances in deep learning have significantly improved emotion detection capabilities, existing methods often fall short because of their exclusive focus on either holistic visual features or semantic content, thereby neglecting their interplay. To address this limitation, we introduce BOVIS, a Bias-Mitigated Object-Enhanced Visual Emotion Analysis framework. To capture the subtle relationships between visual and semantic features and enrich the understanding of emotional contexts, BOVIS leverages pre-trained models to extract comprehensive image features, integrate object-level semantics, and enhance contextual information. Moreover, BOVIS incorporates a bias mitigation strategy that involves an adjusted Mean Absolute Error loss function alongside an Inverse Probability Weighting method to address dataset imbalances and enhance fairness in emotion prediction. Comprehensive evaluations across various benchmark datasets demonstrate the effectiveness of the BOVIS framework in enhancing visual emotion analysis. The results reveal that the synergy between object-specific features and holistic visual representations improves the accuracy and interpretability of emotion analysis, while optimizing bias mitigation enhances fairness and increases reliability. The code is available at https://github.com/leeyubin10/BOVIS.git.

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