# A Circular-Structured Representation for Visual Emotion Distribution Learning

arXiv: https://arxiv.org/abs/2106.12450 · HTML: https://mlsanity.com/paper/2106.12450/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Label Distribution Learning](https://mlsanity.com/paper/1408.6027/) (2014)
- [Deep Label Distribution Learning with Label Ambiguity](https://mlsanity.com/paper/1611.01731/) (2016)
- [Effective Aesthetics Prediction with Multi-level Spatially Pooled Features](https://mlsanity.com/paper/1904.01382/) (2019)
- [Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks](https://mlsanity.com/paper/1509.06041/) (2015)
- [Learning Multi-level Deep Representations for Image Emotion Classification](https://mlsanity.com/paper/1611.07145/) (2016)
- [Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment](https://mlsanity.com/paper/2004.03015/) (2020)
- ["Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attention](https://mlsanity.com/paper/1807.03871/) (2018)
- [AMNet: Memorability Estimation with Attention](https://mlsanity.com/paper/1804.03115/) (2018)
- [Changing the Image Memorability: From Basic Photo Editing to GANs](https://mlsanity.com/paper/1811.03825/) (2018)

## Built on by (most cited first, 17 of 17)

- [Hybrid Curriculum Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2112.11718/) (2021)
- [SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network](https://mlsanity.com/paper/2110.12334/) (2021)
- [Dual-path Collaborative Generation Network for Emotional Video Captioning](https://mlsanity.com/paper/2408.03006/) (2024)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [Seeking Subjectivity in Visual Emotion Distribution Learning](https://mlsanity.com/paper/2207.11875/) (2022)
- [Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer](https://mlsanity.com/paper/2404.17205/) (2024)
- [egoEMOTION: Egocentric Vision and Physiological Signals for Emotion and Personality Recognition in Real-World Tasks](https://mlsanity.com/paper/2510.22129/) (2025)
- [StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning](https://mlsanity.com/paper/2308.03000/) (2023)
- [eMotions: A Large-Scale Dataset and Audio-Visual Fusion Network for Emotion Analysis in Short-form Videos](https://mlsanity.com/paper/2508.06902/) (2025)
- [Bridging Visual Affective Gap: Borrowing Textual Knowledge by Learning from Noisy Image-Text Pairs](https://mlsanity.com/paper/2511.17103/) (2025)
- [Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition](https://mlsanity.com/paper/2602.20530/) (2026)
- [Adaptive Emotional Video Captioning via Affective Heterogeneous Graph Reasoning and Multi-task Joint Learning](https://mlsanity.com/paper/2607.29045/) (2026)
- [AffectDelta: Beyond Emotion Labels for Image Editing](https://mlsanity.com/paper/2609.02616/) (2026)
- [Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context](https://mlsanity.com/paper/2507.12889/) (2025)
- [Free Energy-Based Modeling of Emotional Dynamics in Video Advertisements](https://mlsanity.com/paper/2601.00812/) (2025)
- [Are Emotions Arranged in a Circle? Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning](https://mlsanity.com/paper/2601.06575/) (2026)
- [Dimensional Distribution Emotion State: Leveraging Valence and Arousal as a Common Embedding Space for Visual Emotion Analysis](https://mlsanity.com/paper/2605.26262/) (2026)

## Abstract (arXiv)

Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks. Since human emotions are ambiguous and subjective, it is more reasonable to address VEA in a label distribution learning (LDL) paradigm rather than a single-label classification task. Different from other LDL tasks, there exist intrinsic relationships between emotions and unique characteristics within them, as demonstrated in psychological theories. Inspired by this, we propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes (i.e., emotion polarity, emotion type, emotion intensity) as well as two properties (i.e., similarity, additivity). Besides, we design a novel Progressive Circular (PC) loss to penalize the dissimilarities between predicted emotion vector and labeled one in a coarse-to-fine manner, which further boosts the learning process in an emotion-specific way. Extensive experiments and comparisons are conducted on public visual emotion distribution datasets, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.