# Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases

arXiv: https://arxiv.org/abs/1909.12911 · HTML: https://mlsanity.com/paper/1909.12911/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [Most Important Person-guided Dual-branch Cross-Patch Attention for Group Affect Recognition](https://mlsanity.com/paper/2212.07055/) (2022)
- [Graph Neural Networks in Computer Vision -- Architectures, Datasets and Common Approaches](https://mlsanity.com/paper/2212.10207/) (2022)
- [Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning](https://mlsanity.com/paper/2310.04306/) (2023)
- [Multimodal Group Emotion Recognition In-the-wild Using Privacy-Compliant Features](https://mlsanity.com/paper/2312.05265/) (2023)
- [A Survey of Deep Learning for Group-level Emotion Recognition](https://mlsanity.com/paper/2408.15276/) (2024)
- [Incorporating Scene Context and Semantic Labels for Enhanced Group-level Emotion Recognition](https://mlsanity.com/paper/2509.21747/) (2025)
- [PETA: Photo Albums Event Recognition using Transformers Attention](https://mlsanity.com/paper/2109.12499/) (2021)
- [Improving User Behavior Prediction: Leveraging Annotator Metadata in Supervised Machine Learning Models](https://mlsanity.com/paper/2503.21000/) (2025)
- [Variational Encoder--Multi-Decoder (VE-MD) for Privacy-by-functional-design (Group) Emotion Recognition](https://mlsanity.com/paper/2604.02397/) (2026)
- [GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videos](https://mlsanity.com/paper/2604.16214/) (2026)
- [LG-GER: Language-Guided Group Emotion Recognition via Multimodal Evidence Distillation](https://mlsanity.com/paper/2608.23880/) (2026)

## Abstract (arXiv)

A graph neural network (GNN) for image understanding based on multiple cues is proposed in this paper. Compared to traditional feature and decision fusion approaches that neglect the fact that features can interact and exchange information, the proposed GNN is able to pass information among features extracted from different models. Two image understanding tasks, namely group-level emotion recognition (GER) and event recognition, which are highly semantic and require the interaction of several deep models to synthesize multiple cues, were selected to validate the performance of the proposed method. It is shown through experiments that the proposed method achieves state-of-the-art performance on the selected image understanding tasks. In addition, a new group-level emotion recognition database is introduced and shared in this paper.