# Predicting Group Cohesiveness in Images

arXiv: https://arxiv.org/abs/1812.11771 · HTML: https://mlsanity.com/paper/1812.11771/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey](https://mlsanity.com/paper/2207.10574/) (2022)
- [Most Important Person-guided Dual-branch Cross-Patch Attention for Group Affect Recognition](https://mlsanity.com/paper/2212.07055/) (2022)
- [A Survey of Deep Learning for Group-level Emotion Recognition](https://mlsanity.com/paper/2408.15276/) (2024)
- [MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing](https://mlsanity.com/paper/2409.07256/) (2024)
- [Gems: Group Emotion Profiling Through Multimodal Situational Understanding](https://mlsanity.com/paper/2507.22393/) (2025)

## Abstract (arXiv)

The cohesiveness of a group is an essential indicator of the emotional state, structure and success of a group of people. We study the factors that influence the perception of group-level cohesion and propose methods for estimating the human-perceived cohesion on the group cohesiveness scale. In order to identify the visual cues (attributes) for cohesion, we conducted a user survey. Image analysis is performed at a group-level via a multi-task convolutional neural network. For analyzing the contribution of facial expressions of the group members for predicting the Group Cohesion Score (GCS), a capsule network is explored. We add GCS to the Group Affect database and propose the `GAF-Cohesion database'. The proposed model performs well on the database and is able to achieve near human-level performance in predicting a group's cohesion score. It is interesting to note that group cohesion as an attribute, when jointly trained for group-level emotion prediction, helps in increasing the performance for the later task. This suggests that group-level emotion and cohesion are correlated.