StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning

• URL: http://arxiv.org/abs/2308.03000v1
• Date: Sun, 6 Aug 2023 03:22:46 GMT
• Title: StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning
• Authors: Peiguang Jing, Xianyi Liu, Ji Wang, Yinwei Wei, Liqiang Nie, Yuting Su
• Abstract summary: We propose a style-guided high-order attention network for image emotion distribution learning termed StyleEDL. StyleEDL interactively learns stylistic-aware representations of images by exploring the hierarchical stylistic information of visual contents. In addition, we introduce a stylistic graph convolutional network to dynamically generate the content-dependent emotion representations.
• Score: 69.06749934902464
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion distribution learning has gained increasing attention with the tendency to express emotions through images. As for emotion ambiguity arising from humans' subjectivity, substantial previous methods generally focused on learning appropriate representations from the holistic or significant part of images. However, they rarely consider establishing connections with the stylistic information although it can lead to a better understanding of images. In this paper, we propose a style-guided high-order attention network for image emotion distribution learning termed StyleEDL, which interactively learns stylistic-aware representations of images by exploring the hierarchical stylistic information of visual contents. Specifically, we consider exploring the intra- and inter-layer correlations among GRAM-based stylistic representations, and meanwhile exploit an adversary-constrained high-order attention mechanism to capture potential interactions between subtle visual parts. In addition, we introduce a stylistic graph convolutional network to dynamically generate the content-dependent emotion representations to benefit the final emotion distribution learning. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness of our proposed StyleEDL compared to state-of-the-art methods. The implementation is released at: https://github.com/liuxianyi/StyleEDL.

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