# GROWL: Group Detection With Link Prediction

arXiv: https://arxiv.org/abs/2111.04397 · HTML: https://mlsanity.com/paper/2111.04397/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Semantic Object Parsing with Graph LSTM](https://mlsanity.com/paper/1603.07063/) (2016)
- [SALSA: A Novel Dataset for Multimodal Group Behavior Analysis](https://mlsanity.com/paper/1506.06882/) (2015)
- [F-formation Detection: Individuating Free-standing Conversational Groups in Images](https://mlsanity.com/paper/1409.2702/) (2014)
- [REFORM: Recognizing F-formations for Social Robots](https://mlsanity.com/paper/2008.07668/) (2020)

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

- [Interaction-aware Joint Attention Estimation Using People Attributes](https://mlsanity.com/paper/2308.05382/) (2023)
- [Conversation Group Detection With Spatio-Temporal Context](https://mlsanity.com/paper/2206.02559/) (2022)
- [Learning Social Cost Functions for Human-Aware Path Planning](https://mlsanity.com/paper/2407.10547/) (2024)

## Abstract (arXiv)

Interaction group detection has been previously addressed with bottom-up approaches which relied on the position and orientation information of individuals. These approaches were primarily based on pairwise affinity matrices and were limited to static, third-person views. This problem can greatly benefit from a holistic approach based on Graph Neural Networks (GNNs) beyond pairwise relationships, due to the inherent spatial configuration that exists between individuals who form interaction groups. Our proposed method, GROup detection With Link prediction (GROWL), demonstrates the effectiveness of a GNN based approach. GROWL predicts the link between two individuals by generating a feature embedding based on their neighbourhood in the graph and determines whether they are connected with a shallow binary classification method such as Multi-layer Perceptrons (MLPs). We test our method against other state-of-the-art group detection approaches on both a third-person view dataset and a robocentric (i.e., egocentric) dataset. In addition, we propose a multimodal approach based on RGB and depth data to calculate a representation GROWL can utilise as input. Our results show that a GNN based approach can significantly improve accuracy across different camera views, i.e., third-person and egocentric views.