Papers by Dimosthenis Kontogiorgos

4 papers
A Multimodal Corpus for Mutual Gaze and Joint Attention in Multiparty Situated Interaction (L18-1)

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Challenge: Using a multisensory setup, we capture speech, eye gaze and gesture data and investigate four different types of social gaze: referential gaze, joint attention, mutual gaze and gaze aversion by both perspectives of a speaker and a listener.
Approach: They present a corpus of situated interaction where participants collaborated on moving virtual objects on a large touch screen.
Outcome: The authors capture speech, eye gaze and gesture data using a multisensory setup and analysed the groups' referential eye-gaze with respect to the referent object.
FARMI: A FrAmework for Recording Multi-Modal Interactions (L18-1)

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Challenge: a new framework for recording multi-modal data is needed to capture multi-party, richly recorded corpora and perform real-time processing of such data.
Approach: They propose an open-source processing architecture for corpora and real-time processing . they deploy the architecture in a multi-party deception game with six humans and one robot .
Outcome: The proposed architecture is agnostic to hardware and programming languages, although it's mostly written in Python.
Chinese Whispers: A Multimodal Dataset for Embodied Language Grounding (2020.lrec-1)

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Challenge: In this paper, we introduce a multimodal dataset in which subjects are instructing each other how to assemble IKEA furniture.
Approach: They propose a multimodal dataset in which subjects are instructing each other how to assemble IKEA furniture.
Outcome: The proposed method avoids implicit experimenter biases by allowing subjects to instruct each other on the nature of the task: the process of the furniture assembly.
Crowdsourced Multimodal Corpora Collection Tool (L18-1)

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Challenge: a crowd-sourced corpora recording method has several disadvantages, including the cost of staff, equipment and time spent recording in-lab.
Approach: They propose to use a crowd-sourced data collection tool to gather controlled multimodal data of people in a rapid and scalable fashion.
Outcome: The proposed tool will allow researchers to quickly gather large amounts of multimodal data spanning a wide demographic range and create their own multimodal corpus.

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