# Interaction-GCN: A Graph Convolutional Network based framework for social interaction recognition in egocentric videos

arXiv: https://arxiv.org/abs/2104.14007 · HTML: https://mlsanity.com/paper/2104.14007/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Videos as Space-Time Region Graphs](https://mlsanity.com/paper/1806.01810/) (2018)
- [Seeing Invisible Poses: Estimating 3D Body Pose from Egocentric Video](https://mlsanity.com/paper/1603.07763/) (2016)
- [Joint Inference of Groups, Events and Human Roles in Aerial Videos](https://mlsanity.com/paper/1505.05957/) (2015)
- [Dual-Glance Model for Deciphering Social Relationships](https://mlsanity.com/paper/1708.00634/) (2017)
- [A Domain Based Approach to Social Relation Recognition](https://mlsanity.com/paper/1704.06456/) (2017)
- [With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams](https://mlsanity.com/paper/1605.04129/) (2016)
- [Towards social pattern characterization in egocentric photo-streams](https://mlsanity.com/paper/1709.01424/) (2017)
- [Social Relation Recognition in Egocentric Photostreams](https://mlsanity.com/paper/1905.04734/) (2019)
- [All the people around me: face discovery in egocentric photo-streams](https://mlsanity.com/paper/1703.01790/) (2017)

## Built on by (most cited first, 1 of 1)

- [EgoSocial: Benchmarking Proactive Intervention Ability of Omnimodal LLMs via Egocentric Social Interaction Perception](https://mlsanity.com/paper/2510.13105/) (2025)

## Abstract (arXiv)

In this paper we propose a new framework to categorize social interactions in egocentric videos, we named InteractionGCN. Our method extracts patterns of relational and non-relational cues at the frame level and uses them to build a relational graph from which the interactional context at the frame level is estimated via a Graph Convolutional Network based approach. Then it propagates this context over time, together with first-person motion information, through a Gated Recurrent Unit architecture. Ablation studies and experimental evaluation on two publicly available datasets validate the proposed approach and establish state of the art results.