  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.

Computer Vision and Pattern Recognition;Multimedia