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Emotion-wise feature interaction analysis-based visual emotion distribution learning

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Abstract

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.

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Funding

This study was funded by the Nature Science Foundation of Shanghai (Grant Number 22ZR1418400).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China

    Jing Zhang, Qiuge Qin, Xinyu Liu & Qi Ye

  2. DS Information Technology Co. Ltd, Shanghai, China

    Wen Du

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  1. Jing Zhang
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  2. Qiuge Qin
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  3. Xinyu Liu
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  4. Qi Ye
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Correspondence to Jing Zhang or Qi Ye.

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Author Jing Zhang declares that she has no conflict of interest. Author Qiuge Qin declares that she has no conflict of interest. Author Xinyu Liu declares that he has no conflict of interest. Author Qi Ye declares that she has no conflict of interest. Author Wen Du declares that he has no conflict of interest.

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Zhang, J., Qin, Q., Liu, X. et al. Emotion-wise feature interaction analysis-based visual emotion distribution learning. Vis Comput 40, 1359–1368 (2024). https://doi.org/10.1007/s00371-023-02854-6

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