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Emotion distribution learning has gained increasing attention with thetendency to express emotions through images. As for emotion ambiguity arisingfrom humans' subjectivity, substantial previous methods generally focused onlearning appropriate representations from the holistic or significant part ofimages. However, they rarely consider establishing connections with thestylistic information although it can lead to a better understanding of images.In this paper, we propose a style-guided high-order attention network for imageemotion distribution learning termed StyleEDL, which interactively learnsstylistic-aware representations of images by exploring the hierarchicalstylistic information of visual contents. Specifically, we consider exploringthe intra- and inter-layer correlations among GRAM-based stylisticrepresentations, and meanwhile exploit an adversary-constrained high-orderattention mechanism to capture potential interactions between subtle visualparts. In addition, we introduce a stylistic graph convolutional network todynamically generate the content-dependent emotion representations to benefitthe final emotion distribution learning. Extensive experiments conducted onseveral benchmark datasets demonstrate the effectiveness of our proposedStyleEDL compared to state-of-the-art methods. The implementation is releasedat: https://github.com/liuxianyi/StyleEDL.

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