# Computational Emotion Analysis From Images: Recent Advances and Future Directions

arXiv: https://arxiv.org/abs/2103.10798 · HTML: https://mlsanity.com/paper/2103.10798/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 92 references that are in the map.
- First posted on arXiv 19 Mar 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Learning from Simulated and Unsupervised Images through Adversarial Training](https://mlsanity.com/paper/1612.07828/) (2016)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Deep Label Distribution Learning with Label Ambiguity](https://mlsanity.com/paper/1611.01731/) (2016)
- [A Review of Single-Source Deep Unsupervised Visual Domain Adaptation](https://mlsanity.com/paper/2009.00155/) (2020)
- [Context Based Emotion Recognition using EMOTIC Dataset](https://mlsanity.com/paper/2003.13401/) (2020)
- [Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark](https://mlsanity.com/paper/1605.02677/) (2016)
- [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)
- [An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos](https://mlsanity.com/paper/2003.00832/) (2020)
- [Affective Computing for Large-Scale Heterogeneous Multimedia Data: A Survey](https://mlsanity.com/paper/1911.05609/) (2019)
- [What Twitter Profile and Posted Images Reveal About Depression and Anxiety](https://mlsanity.com/paper/1904.02670/) (2019)
- [PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression](https://mlsanity.com/paper/1909.05693/) (2019)
- [Emotion-Based End-to-End Matching Between Image and Music in Valence-Arousal Space](https://mlsanity.com/paper/2009.05103/) (2020)
- [Visual Sentiment Prediction with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1411.5731/) (2014)
- [Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation](https://mlsanity.com/paper/2011.12470/) (2020)

## Built on by (most cited first, 6 of 6)

- [Facial expression and attributes recognition based on multi-task learning of lightweight neural networks](https://mlsanity.com/paper/2103.17107/) (2021)
- [Learning to Compose Diversified Prompts for Image Emotion Classification](https://mlsanity.com/paper/2201.10963/) (2022)
- [ViPE: Visualise Pretty-much Everything](https://mlsanity.com/paper/2310.10543/) (2023)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [Level of agreement between emotions generated by Artificial Intelligence and human evaluation: a methodological proposal](https://mlsanity.com/paper/2410.08332/) (2024)
- [Finding Beautiful and Happy Images for Mental Health and Well-being Applications](https://mlsanity.com/paper/2404.18109/) (2024)

## Abstract (arXiv)

Emotions are usually evoked in humans by images. Recently, extensive research efforts have been dedicated to understanding the emotions of images. In this chapter, we aim to introduce image emotion analysis (IEA) from a computational perspective with the focus on summarizing recent advances and suggesting future directions. We begin with commonly used emotion representation models from psychology. We then define the key computational problems that the researchers have been trying to solve and provide supervised frameworks that are generally used for different IEA tasks. After the introduction of major challenges in IEA, we present some representative methods on emotion feature extraction, supervised classifier learning, and domain adaptation. Furthermore, we introduce available datasets for evaluation and summarize some main results. Finally, we discuss some open questions and future directions that researchers can pursue.