Dominike Thomas

Institute for Visualisation and Interactive Systems, University of Stuttgart

+49 711 685 60051 Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.035 Google Scholar

Biography

With her experience as undergraduate and graduate student, research coordinator, and editor, Dominike Thomas understands the demands placed on researchers at various steps of the academic publishing process. In October 2021, she decided to fully transition from PhD student to research assistant in the PUI group, to use her experience and linguistic strengths to help her colleagues disseminate their research results. Her new tasks include copy editing of the group’s publications, teaching support, and maintaining the group’s website and social media presence.

Dominike is also coordinator to the Stuttgart ELLIS Unit.

Teaching

2021

Machine Learning and Computer Vision for HCI (Fachpraktikum)Master

2020

Intelligent User Interfaces (Hauptseminar) Master

2019

Medieninformatik (Teaching Assistant)Bachelor

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Supervision

2021

Investigating Human-AI Collaboration in a Maze-Solving RL Environment (B.Sc.) Hendrik Henkelmann

 

Facial Emotion Recognition Behind Face-Masks (InfoTech Project) Nidhi Joshi

 

Low Complexity Conversion of Emotional Signals (M.Sc.) Fabian Maurer

 

Detecting Sarcasm with Commonsense Reasoning in the Multimodal Paradigm of Text and Speech (M.Sc.) Meghdut Sengupta

 

Multimodal Dataset for Deception Detection in a Political Setting (B.Sc.) Timo Hüttner

 

Low Complexity Emotional Transfer (M.Sc.) Cagri Tasci

2020

Incorporating Gaze and Speech Signals in Multi-modal Emotion Recognition (B.Sc.) Ahmed Abdou

 

Classification Between Acted & Natural Emotions Using Audio-Video Feature Fusion (M.Sc.) Gauri Rasane

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Publications

(Müller et al., 2022; Müller et al., 2021; Müller et al., 2022; Strohm et al., 2022; Sood et al., 2023)

1. Multimodal Integration of Human-Like Attention in Visual Question Answering

   Ekta Sood, Fabian Kögel, Philipp Müller, Dominike Thomas, Mihai Bâce, Andreas Bulling

   Proc. Workshop on Gaze Estimation and Prediction in the Wild (GAZE), pp. 1–11, 2023.

   Abstract BibTeX Project Oral presentation

   Human-like attention as a supervisory signal to guide neural attention has shown significant promise but is currently limited to uni-modal integration – even for inherently multi-modal tasks such as visual question answering (VQA). We present the Multimodal Human-like Attention Network (MULAN) – the first method for multimodal integration of human-like attention on image and text during training of VQA models. MULAN integrates attention predictions from two state-of-the-art text and image saliency models into neural self-attention layers of a recent transformer-based VQA model. Through evaluations on the challenging VQAv2 dataset, we show that MULAN achieves a new state-of-the-art performance of 73.98% accuracy on test-std and 73.72% on test-dev and, at the same time, has approximately 80% fewer trainable parameters than prior work. Overall, our work underlines the potential of integrating multimodal human-like and neural attention for VQA.

   @inproceedings{sood23_gaze, author = {Sood, Ekta and Kögel, Fabian and Müller, Philipp and Thomas, Dominike and Bâce, Mihai and Bulling, Andreas}, title = {Multimodal Integration of Human-Like Attention in Visual Question Answering}, booktitle = {Proc. Workshop on Gaze Estimation and Prediction in the Wild (GAZE)}, year = {2023}, pages = {1--11} }

1. 

   MultiMediate’22: Backchannel Detection and Agreement Estimation in Group Interactions

   Philipp Müller, Dominik Schiller, Dominike Thomas, Michael Dietz, Hali Lindsay, Patrick Gebhard, Elisabeth André, Andreas Bulling

   Proc. ACM Multimedia (MM), pp. 7109-7114, 2022.

   Abstract Links BibTeX Project

   Backchannels, i.e. short interjections of the listener, serve important meta-conversational purposes like signifying attention or indicating agreement. Despite their key role, automatic analysis of backchannels in group interactions has been largely neglected so far. The MultiMediate challenge addresses, for the first time, the tasks of backchannel detection and agreement estimation from backchannels in group conversations. This paper describes the MultiMediate challenge and presents a novel set of annotations consisting of 7234 backchannel instances for the MPIIGroup Interaction dataset. Each backchannel was additionally annotated with the extent by which it expresses agreement towards the current speaker. In addition to a an analysis of the collected annotations, we present baseline results for both challenge tasks.

   doi: 10.1145/3503161.3551589

   Paper: mueller22_mm.pdf

   @inproceedings{mueller22_mm, title = {MultiMediate'22: Backchannel Detection and Agreement Estimation in Group Interactions}, author = {M{\"{u}}ller, Philipp and Schiller, Dominik and Thomas, Dominike and Dietz, Michael and Lindsay, Hali and Gebhard, Patrick and André, Elisabeth and Bulling, Andreas}, year = {2022}, pages = {7109-7114}, doi = {10.1145/3503161.3551589}, booktitle = {Proc. ACM Multimedia (MM)} }
2. 

   Facial Composite Generation with Iterative Human Feedback

   Florian Strohm, Ekta Sood, Dominike Thomas, Mihai Bâce, Andreas Bulling

   Proceedings of the NeurIPS Workshop Gaze Meets ML (GMML), pp. 1–19, 2022.

   Abstract Links BibTeX Project Oral presentation

   We propose the first method in which human and AI collaborate to iteratively reconstruct the human’s mental image of another person’s face only from their eye gaze. Current tools for generating digital human faces involve a tedious and time-consuming manual design process. While gaze-based mental image reconstruction represents a promising alternative, previous methods still assumed prior knowledge about the target face, thereby severely limiting their practical usefulness. The key novelty of our method is a collaborative, iterative query engine: Based on the user’s gaze behaviour in each iteration, our method predicts which images to show to the user in the next iteration. Results from two human studies (N=12 and N=22) show that our method can visually reconstruct digital faces that are more similar to the mental image, and is more usable compared to other methods. As such, our findings point at the significant potential of human-AI collaboration for reconstructing mental images, potentially also beyond faces, and of human gaze as a rich source of information and a powerful mediator in said collaboration.

   doi:

   Paper: strohm22_gmml.pdf

   @inproceedings{strohm22_gmml, title = {Facial Composite Generation with Iterative Human Feedback}, author = {Strohm, Florian and Sood, Ekta and Thomas, Dominike and Bâce, Mihai and Bulling, Andreas}, year = {2022}, booktitle = {Proceedings of the NeurIPS Workshop Gaze Meets ML (GMML)}, doi = {}, pages = {1--19} }

1. 

   MultiMediate’22: Backchannel Detection and Agreement Estimation in Group Interactions

   Philipp Müller, Dominik Schiller, Dominike Thomas, Michael Dietz, Hali Lindsay, Patrick Gebhard, Elisabeth André, Andreas Bulling

   arXiv:2209.09578, pp. 1–6, 2022.

   Abstract Links BibTeX Project

   Backchannels, i.e. short interjections of the listener, serve important meta-conversational purposes like signifying attention or indicating agreement. Despite their key role, automatic analysis of backchannels in group interactions has been largely neglected so far. The MultiMediate challenge addresses, for the first time, the tasks of backchannel detection and agreement estimation from backchannels in group conversations. This paper describes the MultiMediate challenge and presents a novel set of annotations consisting of 7234 backchannel instances for the MPIIGroupInteraction dataset. Each backchannel was additionally annotated with the extent by which it expresses agreement towards the current speaker. In addition to a an analysis of the collected annotations, we present baseline results for both challenge tasks.

   doi: 10.48550/arXiv.2209.09578

   Paper: mueller22_arxiv.pdf

   Paper Access: http://arxiv.org/abs/2209.09578

   @techreport{mueller22_arxiv, title = {MultiMediate'22: Backchannel Detection and Agreement Estimation in Group Interactions}, author = {M{\"{u}}ller, Philipp and Schiller, Dominik and Thomas, Dominike and Dietz, Michael and Lindsay, Hali and Gebhard, Patrick and André, Elisabeth and Bulling, Andreas}, year = {2022}, pages = {1--6}, doi = {10.48550/arXiv.2209.09578}, url = {http://arxiv.org/abs/2209.09578} }

1. 

   MultiMediate: Multi-modal Group Behaviour Analysis for Artificial Mediation

   Philipp Müller, Dominik Schiller, Dominike Thomas, Guanhua Zhang, Michael Dietz, Patrick Gebhard, Elisabeth André, Andreas Bulling

   Proc. ACM Multimedia (MM), pp. 4878–4882, 2021.

   Abstract Links BibTeX Project

   Artificial mediators are promising to support human group conversations but at present their abilities are limited by insufficient progress in group behaviour analysis. The MultiMediate challenge addresses, for the first time, two fundamental group behaviour analysis tasks in well-defined conditions: eye contact detection and next speaker prediction. For training and evaluation, MultiMediate makes use of the MPIIGroupInteraction dataset consisting of 22 three- to four-person discussions as well as of an unpublished test set of six additional discussions. This paper describes the MultiMediate challenge and presents the challenge dataset including novel fine-grained speaking annotations that were collected for the purpose of MultiMediate. Furthermore, we present baseline approaches and ablation studies for both challenge tasks.

   doi: 10.1145/3474085.3479219

   Paper: mueller21_mm.pdf

   @inproceedings{mueller21_mm, title = {MultiMediate: Multi-modal Group Behaviour Analysis for Artificial Mediation}, author = {M{\"{u}}ller, Philipp and Schiller, Dominik and Thomas, Dominike and Zhang, Guanhua and Dietz, Michael and Gebhard, Patrick and André, Elisabeth and Bulling, Andreas}, year = {2021}, pages = {4878--4882}, doi = {10.1145/3474085.3479219}, booktitle = {Proc. ACM Multimedia (MM)} }