research-article

Improving Social Awareness Through DANTE: Deep Affinity Network for Clustering Conversational Interactants

Authors:

Mason Swofford,

Sydney Thompson,

Roberto Martín-Martín,

Silvio Savarese,

Marynel VázquezAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 4, Issue CSCW1

Article No.: 20, Pages 1 - 23

https://doi.org/10.1145/3392824

Published: 29 May 2020 Publication History

Abstract

We propose a data-driven approach to detect conversational groups by identifying spatial arrangements typical of these focused social encounters. Our approach uses a novel Deep Affinity Network (DANTE) to predict the likelihood that two individuals in a scene are part of the same conversational group, considering their social context. The predicted pair-wise affinities are then used in a graph clustering framework to identify both small (e.g., dyads) and large groups. The results from our evaluation on multiple, established benchmarks suggest that combining powerful deep learning methods with classical clustering techniques can improve the detection of conversational groups in comparison to prior approaches. Finally, we demonstrate the practicality of our approach in a human-robot interaction scenario. Our efforts show that our work advances group detection not only in theory, but also in practice.

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Cited By

Barua HMg TPramanick PSarkar C(2024)Enabling Social Robots to Perceive and Join Socially Interacting Groups Using F-formation: A Comprehensive OverviewACM Transactions on Human-Robot Interaction10.1145/368207213:4(1-48)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3682072
Li Z(2024)Towards Automatic Social Involvement EstimationProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3688615(612-616)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3688615
Yu JYang JYang HPan RLai PZhai G(2024)Psychology-Guided Environment Aware Network for Discovering Social Interaction Groups from VideosACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365729520:8(1-23)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3657295
Show More Cited By

Index Terms

Improving Social Awareness Through DANTE: Deep Affinity Network for Clustering Conversational Interactants
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Spatial and physical reasoning
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 4, Issue CSCW1

CSCW

May 2020

1285 pages

EISSN:2573-0142

DOI:10.1145/3403424

Editors:
Cliff Lampe
University of Michigan
,
Jeff Nichols
Google
,
Karrie Karahalios
University of Illinois Urbana-Champaign
,
Geraldine Fitzpatrick
Vienna University of Technology
,
Uichin Lee
KAIST
,
Andres Monroy-Hernandez
Microsoft Research
,
Wolfgang Stuerzlinger
Simon Fraser University, Canada

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Publication History

Published: 29 May 2020

Published in PACMHCI Volume 4, Issue CSCW1

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Cited By

Barua HMg TPramanick PSarkar C(2024)Enabling Social Robots to Perceive and Join Socially Interacting Groups Using F-formation: A Comprehensive OverviewACM Transactions on Human-Robot Interaction10.1145/368207213:4(1-48)Online publication date: 23-Oct-2024
https://dl.acm.org/doi/10.1145/3682072
Li Z(2024)Towards Automatic Social Involvement EstimationProceedings of the 26th International Conference on Multimodal Interaction10.1145/3678957.3688615(612-616)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3678957.3688615
Yu JYang JYang HPan RLai PZhai G(2024)Psychology-Guided Environment Aware Network for Discovering Social Interaction Groups from VideosACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365729520:8(1-23)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3657295
Mirsky RXiao XHart JStone P(2024)Conflict Avoidance in Social Navigation—a SurveyACM Transactions on Human-Robot Interaction10.1145/364798313:1(1-36)Online publication date: 12-Feb-2024
https://dl.acm.org/doi/10.1145/3647983
Gillet SVázquez MAndrist SLeite ISebo S(2024)Interaction-Shaping Robotics: Robots That Influence Interactions between Other AgentsACM Transactions on Human-Robot Interaction10.1145/364380313:1(1-23)Online publication date: 2-Feb-2024
https://dl.acm.org/doi/10.1145/3643803
Johansen SBrophy CRittenbruch MDonovan J(2024)Characterising CSCW Research on Human-Robot CollaborationProceedings of the ACM on Human-Computer Interaction10.1145/36409998:CSCW1(1-31)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3640999
Webb NGiuliani MLemaignan SGrollman DBroadbent EJu WSoh HWilliams T(2024)Measuring Visual Social Engagement from Proxemics and Gaze in the Real WorldCompanion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610978.3640678(1110-1113)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610978.3640678
Taylor ATanjim TCao HLee HGrollman DBroadbent EJu WSoh HWilliams T(2024)Towards Collaborative Crash Cart Robots that Support Clinical TeamworkProceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610977.3634967(715-724)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610977.3634967
Nasri MMaliappis TRieffe CBaratchi M(2024)T-DANTE: Detecting Group Behaviour in Spatio-Temporal Trajectories Using Context InformationAdvances in Intelligent Data Analysis XXII10.1007/978-3-031-58553-1_3(28-39)Online publication date: 16-Apr-2024
https://doi.org/10.1007/978-3-031-58553-1_3
Hedayati HMuehlbradt AKennedy JSzafir D(2023)Identifying the Focus of Attention in Human-Robot Conversational GroupsProceedings of the 11th International Conference on Human-Agent Interaction10.1145/3623809.3623866(3-12)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3623809.3623866
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