VEMI Lab 2021

The Virtual Environments and Multimodal Interaction (VEMI) Lab embodies an inclusive, collaborative, and multi-disciplinary approach to hands-on research and education. By bringing together students and faculty from more than a dozen majors and disciplines, VEMI is uniquely positioned to advance computing and STEM initiatives both here at the university as well as in broader communities throughout Maine and nationwide

Deep multimodal fusion : combining discrete events and continuous signals

Multimodal datasets often feature a combination of continuous signals and a series of discrete events. For instance, when studying human behaviour it is common to annotate actions performed by the participant over several other modalities such as video recordings of the face or physiological signals. These events are nominal, not frequent and are not sampled at a continuous rate while signals are numeric and often sampled at short fixed intervals. This fundamentally different nature complicates the analysis of the relation among these modalities which is often studied after each modality has been summarised or reduced. This paper investigates a novel approach to model the relation between such modality types bypassing the need for summarising each modality independently of each other. For that purpose, we introduce a deep learning model based on convolutional neural networks that is adapted to process multiple modalities at different time resolutions we name deep multimodal fusion. Furthermore, we introduce and compare three alternative methods (convolution, training and pooling fusion) to integrate sequences of events with continuous signals within this model. We evaluate deep multimodal fusion using a game user dataset where player physiological signals are recorded in parallel with game events. Results suggest that the proposed architecture can appropriately capture multimodal information as it yields higher prediction accuracies compared to single-modality models. In addition, it appears that pooling fusion, based on a novel filter-pooling method provides the more effective fusion approach for the investigated types of data.peer-reviewe

Introduction: Multimodal interaction

That human social interaction involves the intertwined cooperation of different modalities is uncontroversial. Researchers in several allied fields have, however, only recently begun to document the precise ways in which talk, gesture, gaze, and aspects of the material surround are brought together to form coherent courses of action. The papers in this volume are attempts to develop this line of inquiry. Although the authors draw on a range of analytic, theoretical, and methodological traditions (conversation analysis, ethnography, distributed cognition, and workplace studies), all are concerned to explore and illuminate the inherently multimodal character of social interaction. Recent studies, including those collected in this volume, suggest that different modalities work together not only to elaborate the semantic content of talk but also to constitute coherent courses of action. In this introduction we present evidence for this position. We begin by reviewing some select literature focusing primarily on communicative functions and interactive organizations of specific modalities before turning to consider the integration of distinct modalities in interaction

Multimodal Interaction in a Haptic Environment

In this paper we investigate the introduction of haptics in a multimodal tutoring environment. In this environment a haptic device is used to control a virtual piece of sterile cotton and a virtual injection needle. Speech input and output is provided to interact with a virtual tutor, available as a talking head, and a virtual patient. We introduce the haptic tasks and how different agents in the multi-agent system are made responsible for them. Notes are provided about the way we introduce an affective model in the tutor agent

Recurrent Multimodal Interaction for Referring Image Segmentation

In this paper we are interested in the problem of image segmentation given natural language descriptions, i.e. referring expressions. Existing works tackle this problem by first modeling images and sentences independently and then segment images by combining these two types of representations. We argue that learning word-to-image interaction is more native in the sense of jointly modeling two modalities for the image segmentation task, and we propose convolutional multimodal LSTM to encode the sequential interactions between individual words, visual information, and spatial information. We show that our proposed model outperforms the baseline model on benchmark datasets. In addition, we analyze the intermediate output of the proposed multimodal LSTM approach and empirically explain how this approach enforces a more effective word-to-image interaction.Comment: To appear in ICCV 2017. See http://www.cs.jhu.edu/~cxliu/ for code and supplementary materia

A generic architecture and dialogue model for multimodal interaction

This paper presents a generic architecture and a dialogue model for multimodal interaction. Architecture and model are transparent and have been used for different task domains. In this paper the emphasis is on their use for the navigation task in a virtual environment. The dialogue model is based on the information state approach and the recognition of dialogue acts. We explain how pairs of backward and forward looking tags and the preference rules of the dialogue act determiner together determine the structure of the dialogues that can be handled by the system. The system action selection mechanism and the problem of reference resolution are discussed in detail

Description languages for multimodal interaction: a set ofguidelines and its illustration with SMUIML

This article introduces the problem of modeling multimodal interaction, in the form of markup languages. After an analysis of the current state of the art in multimodal interaction description languages, nine guidelines for languages dedicated at multimodal interaction description are introduced, as well as four different roles that such language should target: communication, configuration, teaching and modeling. The article further presents the SMUIML language, our proposed solution to improve the time synchronicity aspect while still fulfilling other guidelines. SMUIML is finally mapped to these guidelines as a way to evaluate their spectrum and to sketch future work