Relevant bibliographies by topics / Multimodal processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multimodal processing'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Multimodal processing"

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Ng, Vincent, and Shengjie Li. "Multimodal Propaganda Processing." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15368–75. http://dx.doi.org/10.1609/aaai.v37i13.26792.

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Propaganda campaigns have long been used to influence public opinion via disseminating biased and/or misleading information. Despite the increasing prevalence of propaganda content on the Internet, few attempts have been made by AI researchers to analyze such content. We introduce the task of multimodal propaganda processing, where the goal is to automatically analyze propaganda content. We believe that this task presents a long-term challenge to AI researchers and that successful processing of propaganda could bring machine understanding one important step closer to human understanding. We di
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Azizbek Akmal ugli, Akramov, and Rakhimberdiev Sanjarbek Alisher ugli. "MULTIMODAL SPEECH PROCESSING." INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY 2, no. 07 (2025): 33–38. https://doi.org/10.70728/tech.v2.i07.0012.

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The aim of this research is to evaluate the effectiveness of multimodal speech processing techniques in improving speech recognition accuracy, addressing the key issue of integrating audio, visual, and contextual cues in real-time applications; to solve this problem, data is required from diverse speech datasets that encompass various modalities, including video input, audio recordings, and contextual language information. This dissertation examines the effectiveness of multimodal speech processing techniques in enhancing speech recognition accuracy, focusing particularly on the integration of
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Sinke, Christopher, Janina Neufeld, Daniel Wiswede, Hinderk M. Emrich, Stefan Bleich, and Gregor R. Szycik. "Multisensory processing in synesthesia — differences in the EEG signal during uni- and multimodal processing." Seeing and Perceiving 25 (2012): 53. http://dx.doi.org/10.1163/187847612x646749.

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Synesthesia is a condition in which stimulation in one processing stream (e.g., letters or music) leads to perception in an unstimulated processing stream (e.g., colors). Behavioral differences in mutisensory processing have been shown for multimodal illusions, but the differences in neural processing are still unclear. In the present study, we examined uni- and multimodal processing in 14 people with synesthesia and 13 controls using EEG recordings and a simple detection task. Stimuli were either presented acoustically, visually or multimodaly (simultaneous visual and auditory stimulation). I
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D'Ulizia, Arianna, Fernando Ferri, and Patrizia Grifoni. "Generating Multimodal Grammars for Multimodal Dialogue Processing." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 40, no. 6 (2010): 1130–45. http://dx.doi.org/10.1109/tsmca.2010.2041227.

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Barricelli, Barbara Rita, Piero Mussio, Marco Padula, and Paolo Luigi Scala. "TMS for multimodal information processing." Multimedia Tools and Applications 54, no. 1 (2010): 97–120. http://dx.doi.org/10.1007/s11042-010-0527-x.

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Parsons, Aaron D., Stephen W. T. Price, Nicola Wadeson, et al. "Automatic processing of multimodal tomography datasets." Journal of Synchrotron Radiation 24, no. 1 (2017): 248–56. http://dx.doi.org/10.1107/s1600577516017756.

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With the development of fourth-generation high-brightness synchrotrons on the horizon, the already large volume of data that will be collected on imaging and mapping beamlines is set to increase by orders of magnitude. As such, an easy and accessible way of dealing with such large datasets as quickly as possible is required in order to be able to address the core scientific problems during the experimental data collection. Savu is an accessible and flexible big data processing framework that is able to deal with both the variety and the volume of data of multimodal and multidimensional scienti
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Holler, Judith, and Stephen C. Levinson. "Multimodal Language Processing in Human Communication." Trends in Cognitive Sciences 23, no. 8 (2019): 639–52. http://dx.doi.org/10.1016/j.tics.2019.05.006.

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Farzin, Faraz, Eric P. Charles, and Susan M. Rivera. "Development of Multimodal Processing in Infancy." Infancy 14, no. 5 (2009): 563–78. http://dx.doi.org/10.1080/15250000903144207.

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Zhang, Ge, Tianxiang Luo, Witold Pedrycz, Mohammed A. El-Meligy, Mohamed Abdel Fattah Sharaf, and Zhiwu Li. "Outlier Processing in Multimodal Emotion Recognition." IEEE Access 8 (2020): 55688–701. http://dx.doi.org/10.1109/access.2020.2981760.

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Metaxakis, Athanasios, Dionysia Petratou, and Nektarios Tavernarakis. "Multimodal sensory processing in Caenorhabditis elegans." Open Biology 8, no. 6 (2018): 180049. http://dx.doi.org/10.1098/rsob.180049.

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Multisensory integration is a mechanism that allows organisms to simultaneously sense and understand external stimuli from different modalities. These distinct signals are transduced into neuronal signals that converge into decision-making neuronal entities. Such decision-making centres receive information through neuromodulators regarding the organism's physiological state and accordingly trigger behavioural responses. Despite the importance of multisensory integration for efficient functioning of the nervous system, and also the implication of dysfunctional multisensory integration in the ae
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Dissertations / Theses on the topic "Multimodal processing"

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Cadène, Rémi. "Deep Multimodal Learning for Vision and Language Processing." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS277.

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Les technologies du numérique ont joué un rôle déterminant dans la transformation de notre société. Des méthodes statistiques récentes ont été déployées avec succès afin d’automatiser le traitement de la quantité croissante d’images, de vidéos et de textes que nous produisons quotidiennement. En particulier, les réseaux de neurones profonds ont été adopté par les communautés de la vision par ordinateur et du traitement du langage naturel pour leur capacité à interpréter le contenu des images et des textes une fois entraînés sur de grands ensembles de données. Les prog
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Hu, Yongtao, and 胡永涛. "Multimodal speaker localization and identification for video processing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/212633.

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Chen, Xun. "Multimodal biomedical signal processing for corticomuscular coupling analysis." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/45811.

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Corticomuscular coupling analysis using multiple data sets such as electroencepha-logram (EEG) and electromyogram (EMG) signals provides a useful tool for understanding human motor control systems. A popular conventional method to assess corticomuscular coupling is the pair-wise magnitude-squared coherence (MSC). However, there are certain limitations associated with MSC, including the difficulty in robustly assessing group inference, only dealing with two types of data sets simultaneously and the biologically implausible assumption of pair-wise interactions. In this thesis, we propose severa
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Sadr, Lahijany Nadi. "Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17636.

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This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorde
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Elshaw, Mark. "Multimodal neural grounding of language processing for robot actions." Thesis, University of Sunderland, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420517.

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Friedel, Paul. "Sensory information processing : detection, feature extraction, & multimodal integration." kostenfrei, 2008. http://mediatum2.ub.tum.de/doc/651333/651333.pdf.

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Sadeghi, Ghandehari Soroush. "Multimodal signal processing in the peripheral and central vestibular pathways." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=95559.

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The vestibular sensory apparatus and associated vestibular nuclei encode head movements during our daily activities. In addition to direct inputs from vestibular afferents, the vestibular nuclei receive substantial projections from cortical. cerebellar, spinal cord. and other brainstem structures. The present studies were aimed at investigating the coding strategies and the signals carried by the peripheral and central vestibular neurons under normal conditions and following vestibular compensation. In normal animals, we first studied the coding strategies of regular and irregular afferents us
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Fateri, Sina. "Advanced signal processing techniques for multimodal ultrasonic guided wave response." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/11657.

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Ultrasonic technology is commonly used in the eld of Non-Destructive Testing (NDT) of metal structures such as steel, aluminium, etc. Compared to ultrasonic bulk waves that travel in infinite media with no boundary influence, Ultrasonic Guided Waves (UGWs) require a structural boundary for propagation such that they can be used to inspect and monitor long elements of a structure from a single position. The greatest challenges for any UGW system are the plethora of wave modes arising from the geometry of the structural element which propagate with a range of frequency dependent velocities and t
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Caglayan, Ozan. "Multimodal Machine Translation." Thesis, Le Mans, 2019. http://www.theses.fr/2019LEMA1016/document.

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La traduction automatique vise à traduire des documents d’une langue à une autre sans l’intervention humaine. Avec l’apparition des réseaux de neurones profonds (DNN), la traduction automatique neuronale(NMT) a commencé à dominer le domaine, atteignant l’état de l’art pour de nombreuses langues. NMT a également ravivé l’intérêt pour la traduction basée sur l’interlangue grâce à la manière dont elle place la tâche dans un cadre encodeur-décodeur en passant par des représentations latentes. Combiné avec la flexibilité architecturale des DNN, ce cadre a aussi ouvert une piste de recherche sur la
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Fridman, Linnea, and Victoria Nordberg. "Two Multimodal Image Registration Approaches for Positioning Purposes." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157424.

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This report is the result of a master thesis made by two students at Linköping University. The aim was to find an image registration method for visual and infrared images and to find an error measure for grading the registration performance. In practice this could be used for position determination by registering the infrared image taken at the current position to a set of visual images with known positions and determining which visual image matches the best. Two methods were tried, using different image feature extractors and different ways to match the features. The first method used phase i
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Books on the topic "Multimodal processing"

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Renals, Steve, Herve Bourlard, Jean Carletta, and Andrei Popescu-Belis, eds. Multimodal Signal Processing. Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9781139136310.

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Maragos, Petros, Alexandros Potamianos, and Patrick Gros, eds. Multimodal Processing and Interaction. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76316-3.

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Renals, Steve. Multimodal signal processing: Human interactions in meetings. Cambridge University Press, 2012.

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Ferran, Marques, Knovel (Firm), and ScienceDirect (Online service), eds. Multimodal signal processing: Theory and applications for human-computer interaction. Academic, 2010.

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Gavrilova, Marina L. Multimodal biometrics and intelligent image processing for security systems. Information Science Reference, 2013.

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Sappa, Angel D. Multimodal Interaction in Image and Video Applications. Springer Berlin Heidelberg, 2013.

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Adams, Teresa M. Guidelines for the implementation of multimodal transportation location referencing systems. National Academy Press, 2001.

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Kühnel, Christine. Quantifying Quality Aspects of Multimodal Interactive Systems. Springer Berlin Heidelberg, 2012.

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Enrique, Vidal, Casacuberta Francisco, and SpringerLink (Online service), eds. Multimodal Interactive Pattern Recognition and Applications. Springer-Verlag London Limited, 2011.

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Wolfgang, Minker, Bühler Dirk MSc, Dybkjær Laila 1959-, and International Society for Computers and Their Applications., eds. Spoken multimodal human-computer dialogue in mobile environments. Springer, 2005.

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Book chapters on the topic "Multimodal processing"

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Huang, Lihe. "Collecting and processing multimodal data." In Toward Multimodal Pragmatics. Routledge, 2021. http://dx.doi.org/10.4324/9781003251774-5.

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Gullberg, Marianne. "Studying Multimodal Language Processing." In The Routledge Handbook of Second Language Acquisition and Psycholinguistics. Routledge, 2022. http://dx.doi.org/10.4324/9781003018872-14.

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Waibel, Alex, Minh Tue Vo, Paul Duchnowski, and Stefan Manke. "Multimodal Interfaces." In Integration of Natural Language and Vision Processing. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-1716-3_9.

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Rivet, Bertrand, and Jonathon Chambers. "Multimodal Speech Separation." In Advances in Nonlinear Speech Processing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11509-7_1.

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Maragos, Petros, Patrick Gros, Athanassios Katsamanis, and George Papandreou. "Cross-Modal Integration for Performance Improving in Multimedia: A Review." In Multimodal Processing and Interaction. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76316-3_1.

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Ferecatu, Marin, Nozha Boujemaa, and Michel Crucianu. "Interactive Image Retrieval Using a Hybrid Visual and Conceptual Content Representation." In Multimodal Processing and Interaction. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76316-3_10.

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Neumayer, Robert, and Andreas Rauber. "Multimodal Analysis of Text and Audio Features for Music Information Retrieval." In Multimodal Processing and Interaction. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76316-3_11.

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Petrakis, Euripides G. M. "Intelligent Search for Image Information on the Web through Text and Link Structure Analysis." In Multimodal Processing and Interaction. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76316-3_12.

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Potamianos, Alexandros, and Manolis Perakakis. "IDesign Principles for Multimodal Spoken Dialogue Systems." In Multimodal Processing and Interaction. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76316-3_13.

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Oyekoya, Oyewole K., and Fred W. M. Stentiford. "Eye Tracking: A New Interface for Visual Exploration." In Multimodal Processing and Interaction. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76316-3_14.

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Conference papers on the topic "Multimodal processing"

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Jain, Vrinda, Thomas T. Livecchi, Abmael H. Oliveira, Steven L. Jacques, Mark C. Pierce, and Hrebesh M. Subhash. "Comprehensive characterization and multimodal image processing with the Polaroid P31 intraoral camera." In Multimodal Biomedical Imaging XX, edited by Xavier Intes, Marien Ochoa, and Mohammad A. Yaseen. SPIE, 2025. https://doi.org/10.1117/12.3043788.

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Xu, Renxin, Yongqi Shao, Zihan Wang, Shijie Yang, Tao Fang, and Hong Huo. "AliSum: Multimodal Summarization with Multimodal Output Boosted by Multimodal Alignment." In 2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP). IEEE, 2024. https://doi.org/10.1109/mlnlp63328.2024.10800131.

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Li, Yinheng, Han Ding, and Hang Chen. "Data Processing Techniques for Modern Multimodal Models." In 2024 IEEE Thirteenth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2024. http://dx.doi.org/10.1109/ipta62886.2024.10755555.

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Yu, Haofei, Zhengyang Qi, Lawrence Keunho Jang, Russ Salakhutdinov, Louis-Philippe Morency, and Paul Pu Liang. "MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.558.

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Grigoryan, Artyom M., Sos S. Agaian, and Samuel White. "A novel approach to image enhancement using a 2x2 processing model for color-to-grayscale mapping." In Multimodal Image Exploitation and Learning 2025, edited by Sos S. Agaian, Stephen P. DelMarco, and Vijayan K. Asari. SPIE, 2025. https://doi.org/10.1117/12.3054070.

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Sunada, Satoshi, Kei Kitagawa, and Tomoaki Niiyama. "Multimodal soft sensor processing based on optical hyperdimensionality." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3027531.

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Zhang, Chengwen, Yaohui Liu, and Bo Cheng. "A MoE Multimodal Graph Attention Network Framework for Multimodal Emotion Recognition." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890390.

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Xuan, Zitao, Xuefeng Jiang, Yu Shi, Zhide Li, and Yuhan Dong. "Multimodal-CanGen: A Multimodal Genome-Based Model for Cancer Type Classification." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906247.

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Zhang, Yu, Bin Chen, Hongfei Ye, et al. "Text-guided Multimodal Fusion for the Multimodal Emotion and Intent Joint Understanding." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890680.

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Wei, Tianyu, He Chen, Jue Wang, and Wenchao Liu. "MDFNet: Multimodal Feature Decomposition and Fusion Network for Multimodal Remote Sensing Image Semantic Segmentation." In 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE, 2024. https://doi.org/10.1109/icsidp62679.2024.10868650.

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Reports on the topic "Multimodal processing"

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Gazzaniga, Michael S. Multimodal Interactions in Sensory-Motor Processing. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada255780.

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Hughes, H. C., P. A. Reuter-Lorenz, R. Fendrich, G. Nozawa, and M. S. Gazzaniga. Multimodal Interactions in Sensory-Motor Processing. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada229111.

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Lewandowski, Lawrence J., Susan B. Hursh, and David A. Kobus. Multimodal versus Unimodal Information Processing of Words. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160517.

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Varshney, Pramod K. Multimodal Signal Processing for Personnel Detection and Activity Classification for Indoor Surveillance. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada606602.

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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from t
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no
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Federal Information Processing Standards Publication: detail specification for 62.5?m core diameter125-?m cladding diameter class IA multimode, graded-index optical waveguide fibers. National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.fips.159.

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Federal Information Processing Standards Publication: detail specification for 62.5?m core diameter125-?m cladding diameter class IA multimode, graded-index optical waveguide fibers. National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.fips.159-1990.

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