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Emotion-wise feature interaction analysis-based visual emotion distribution learning
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AI
DOI：10.1007/s00371-023-02854-6.png)
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Image emotion is not exclusive, which makes emotion distribution learning more meaningful than emotion classification for visual emotion recognition. Consider the emotion correlations implicit in complex images do not strictly follow the universal psychological laws, which is essential for image sentiment analysis. We propose a novel emotion-wise feature interaction analysis (EFIA) method to study the emotion correlations for emotion distribution learning. It facilitates the interaction of specific features categories to learn complicated and specific inter-class relationships from the emotion feature perspective. In addition, we propose a distribution-oriented multi-task learning method to obtain a specialized distribution learning model. Experiments on public emotion datasets illustrate that our proposed method can achieve excellent performance on image emotion distribution learning and outperform most state-of-the-art methods.
Keyword:
Visual emotion distribution learning
Feature interaction analysis
Interclass relationships
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