Mihai Burzo


2022

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In-the-Wild Video Question Answering
Santiago Castro | Naihao Deng | Pingxuan Huang | Mihai Burzo | Rada Mihalcea
Proceedings of the 29th International Conference on Computational Linguistics

Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the “in the wild” settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding dataset of videos recorded in outside settings. In addition to video question answering (Video QA), we also introduce the new task of identifying visual support for a given question and answer (Video Evidence Selection). Through evaluations using a wide range of baseline models, we show that WILDQA poses new challenges to the vision and language research communities. The dataset is available at https: //lit.eecs.umich.edu/wildqa/.

2021

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MUSER: MUltimodal Stress detection using Emotion Recognition as an Auxiliary Task
Yiqun Yao | Michalis Papakostas | Mihai Burzo | Mohamed Abouelenien | Rada Mihalcea
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The capability to automatically detect human stress can benefit artificial intelligent agents involved in affective computing and human-computer interaction. Stress and emotion are both human affective states, and stress has proven to have important implications on the regulation and expression of emotion. Although a series of methods have been established for multimodal stress detection, limited steps have been taken to explore the underlying inter-dependence between stress and emotion. In this work, we investigate the value of emotion recognition as an auxiliary task to improve stress detection. We propose MUSER – a transformer-based model architecture and a novel multi-task learning algorithm with speed-based dynamic sampling strategy. Evaluation on the Multimodal Stressed Emotion (MuSE) dataset shows that our model is effective for stress detection with both internal and external auxiliary tasks, and achieves state-of-the-art results.

2020

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MuSE: a Multimodal Dataset of Stressed Emotion
Mimansa Jaiswal | Cristian-Paul Bara | Yuanhang Luo | Mihai Burzo | Rada Mihalcea | Emily Mower Provost
Proceedings of the Twelfth Language Resources and Evaluation Conference

Endowing automated agents with the ability to provide support, entertainment and interaction with human beings requires sensing of the users’ affective state. These affective states are impacted by a combination of emotion inducers, current psychological state, and various conversational factors. Although emotion classification in both singular and dyadic settings is an established area, the effects of these additional factors on the production and perception of emotion is understudied. This paper presents a new dataset, Multimodal Stressed Emotion (MuSE), to study the multimodal interplay between the presence of stress and expressions of affect. We describe the data collection protocol, the possible areas of use, and the annotations for the emotional content of the recordings. The paper also presents several baselines to measure the performance of multimodal features for emotion and stress classification.

2015

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Verbal and Nonverbal Clues for Real-life Deception Detection
Verónica Pérez-Rosas | Mohamed Abouelenien | Rada Mihalcea | Yao Xiao | CJ Linton | Mihai Burzo
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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A Multimodal Dataset for Deception Detection
Verónica Pérez-Rosas | Rada Mihalcea | Alexis Narvaez | Mihai Burzo
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents the construction of a multimodal dataset for deception detection, including physiological, thermal, and visual responses of human subjects under three deceptive scenarios. We present the experimental protocol, as well as the data acquisition process. To evaluate the usefulness of the dataset for the task of deception detection, we present a statistical analysis of the physiological and thermal modalities associated with the deceptive and truthful conditions. Initial results show that physiological and thermal responses can differentiate between deceptive and truthful states.