@inproceedings{agarwal-etal-2022-model,
title = "Model Transfer for Event tracking as Transcript Understanding for Videos of Small Group Interaction",
author = "Agarwal, Sumit and
Vitiello, Rosanna and
Ros{\'e}, Carolyn",
editor = "Dernoncourt, Franck and
Nguyen, Thien Huu and
Lai, Viet Dac and
Veyseh, Amir Pouran Ben and
Bui, Trung H. and
Yoon, David Seunghyun",
booktitle = "Proceedings of the First Workshop On Transcript Understanding",
month = oct,
year = "2022",
address = "Gyeongju, South Korea",
publisher = "International Conference on Computational Linguistics",
url = "https://aclanthology.org/2022.tu-1.3",
pages = "20--29",
abstract = "Videos of group interactions contain a wealth of information beyond the information directly communicated in a transcript of the discussion. Tracking who has participated throughout an extended interaction and what each of their trajectories has been in relation to one another is the foundation for joint activity understanding, though it comes with some unique challenges in videos of tightly coupled group work. Motivated by insights into the properties of such scenarios, including group composition and the properties of task-oriented, goal directed tasks, we present a successful proof-of-concept. In particular, we present a transfer experiment to a dyadic robot construction task, an ablation study, and a qualitative analysis.",
}
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<abstract>Videos of group interactions contain a wealth of information beyond the information directly communicated in a transcript of the discussion. Tracking who has participated throughout an extended interaction and what each of their trajectories has been in relation to one another is the foundation for joint activity understanding, though it comes with some unique challenges in videos of tightly coupled group work. Motivated by insights into the properties of such scenarios, including group composition and the properties of task-oriented, goal directed tasks, we present a successful proof-of-concept. In particular, we present a transfer experiment to a dyadic robot construction task, an ablation study, and a qualitative analysis.</abstract>
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%0 Conference Proceedings
%T Model Transfer for Event tracking as Transcript Understanding for Videos of Small Group Interaction
%A Agarwal, Sumit
%A Vitiello, Rosanna
%A Rosé, Carolyn
%Y Dernoncourt, Franck
%Y Nguyen, Thien Huu
%Y Lai, Viet Dac
%Y Veyseh, Amir Pouran Ben
%Y Bui, Trung H.
%Y Yoon, David Seunghyun
%S Proceedings of the First Workshop On Transcript Understanding
%D 2022
%8 October
%I International Conference on Computational Linguistics
%C Gyeongju, South Korea
%F agarwal-etal-2022-model
%X Videos of group interactions contain a wealth of information beyond the information directly communicated in a transcript of the discussion. Tracking who has participated throughout an extended interaction and what each of their trajectories has been in relation to one another is the foundation for joint activity understanding, though it comes with some unique challenges in videos of tightly coupled group work. Motivated by insights into the properties of such scenarios, including group composition and the properties of task-oriented, goal directed tasks, we present a successful proof-of-concept. In particular, we present a transfer experiment to a dyadic robot construction task, an ablation study, and a qualitative analysis.
%U https://aclanthology.org/2022.tu-1.3
%P 20-29
Markdown (Informal)
[Model Transfer for Event tracking as Transcript Understanding for Videos of Small Group Interaction](https://aclanthology.org/2022.tu-1.3) (Agarwal et al., TU 2022)
ACL