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

arXiv: https://arxiv.org/abs/2308.03000 · HTML: https://mlsanity.com/paper/2308.03000/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [Deep Label Distribution Learning with Label Ambiguity](https://mlsanity.com/paper/1611.01731/) (2016)
- [Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition](https://mlsanity.com/paper/2012.02994/) (2020)
- [Mixed High-Order Attention Network for Person Re-Identification](https://mlsanity.com/paper/1908.05819/) (2019)
- [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://mlsanity.com/paper/2106.16125/) (2021)
- [ECLARE: Extreme Classification with Label Graph Correlations](https://mlsanity.com/paper/2108.00261/) (2021)
- [Learning Multi-level Deep Representations for Image Emotion Classification](https://mlsanity.com/paper/1611.07145/) (2016)
- [A Circular-Structured Representation for Visual Emotion Distribution Learning](https://mlsanity.com/paper/2106.12450/) (2021)
- [A Geometry-Sensitive Approach for Photographic Style Classification](https://mlsanity.com/paper/1909.01040/) (2019)

## Built on by (most cited first, 2 of 2)

- [VAEER: Visual Attention-Inspired Emotion Elicitation Reasoning](https://mlsanity.com/paper/2505.24342/) (2025)
- [Rethinking Occlusion in FER: A Semantic-Aware Perspective and Go Beyond](https://mlsanity.com/paper/2507.15401/) (2025)

## Abstract (arXiv)

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.