# A Survey of Deep Learning for Group-level Emotion Recognition

arXiv: https://arxiv.org/abs/2408.15276 · HTML: https://mlsanity.com/paper/2408.15276/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 103 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild](https://mlsanity.com/paper/1708.03985/) (2017)
- [Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling](https://mlsanity.com/paper/2102.06183/) (2021)
- [Understanding the Behaviour of Contrastive Loss](https://mlsanity.com/paper/2012.09740/) (2020)
- [Survey on Emotional Body Gesture Recognition](https://mlsanity.com/paper/1801.07481/) (2018)
- [Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection](https://mlsanity.com/paper/1910.10334/) (2019)
- [EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction](https://mlsanity.com/paper/1808.07773/) (2018)
- [Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos](https://mlsanity.com/paper/1811.11849/) (2018)
- [Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases](https://mlsanity.com/paper/1909.12911/) (2019)
- [Generative Emotional AI for Speech Emotion Recognition: The Case for Synthetic Emotional Speech Augmentation](https://mlsanity.com/paper/2301.03751/) (2023)
- [An Attention Model for group-level emotion recognition](https://mlsanity.com/paper/1807.03380/) (2018)
- [Predicting Group Cohesiveness in Images](https://mlsanity.com/paper/1812.11771/) (2018)
- [Most Important Person-guided Dual-branch Cross-Patch Attention for Group Affect Recognition](https://mlsanity.com/paper/2212.07055/) (2022)
- [Group-level Emotion Recognition using Transfer Learning from Face Identification](https://mlsanity.com/paper/1709.01688/) (2017)
- [Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers](https://mlsanity.com/paper/1709.03820/) (2017)
- [Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning](https://mlsanity.com/paper/2310.04306/) (2023)
- [Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach](https://mlsanity.com/paper/2009.07013/) (2020)
- [Multimodal End-to-End Group Emotion Recognition using Cross-Modal Attention](https://mlsanity.com/paper/2111.05890/) (2021)
- [Multimodal Group Emotion Recognition In-the-wild Using Privacy-Compliant Features](https://mlsanity.com/paper/2312.05265/) (2023)
- [Group Emotion Recognition Using Machine Learning](https://mlsanity.com/paper/1905.01118/) (2019)
- [Automatic Group Cohesiveness Detection With Multi-modal Features](https://mlsanity.com/paper/1910.01197/) (2019)

## Built on by (most cited first, 3 of 3)

- [Gems: Group Emotion Profiling Through Multimodal Situational Understanding](https://mlsanity.com/paper/2507.22393/) (2025)
- [Discrete Prompt Tuning via Recursive Utilization of Black-box Multimodal Large Language Model for Personalized Visual Emotion Recognition](https://mlsanity.com/paper/2509.04480/) (2025)
- [GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videos](https://mlsanity.com/paper/2604.16214/) (2026)

## Abstract (arXiv)

With the advancement of artificial intelligence (AI) technology, group-level emotion recognition (GER) has emerged as an important area in analyzing human behavior. Early GER methods are primarily relied on handcrafted features. However, with the proliferation of Deep Learning (DL) techniques and their remarkable success in diverse tasks, neural networks have garnered increasing interest in GER. Unlike individual's emotion, group emotions exhibit diversity and dynamics. Presently, several DL approaches have been proposed to effectively leverage the rich information inherent in group-level image and enhance GER performance significantly. In this survey, we present a comprehensive review of DL techniques applied to GER, proposing a new taxonomy for the field cover all aspects of GER based on DL. The survey overviews datasets, the deep GER pipeline, and performance comparisons of the state-of-the-art methods past decade. Moreover, it summarizes and discuss the fundamental approaches and advanced developments for each aspect. Furthermore, we identify outstanding challenges and suggest potential avenues for the design of robust GER systems. To the best of our knowledge, thus survey represents the first comprehensive review of deep GER methods, serving as a pivotal references for future GER research endeavors.