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Relevant bibliographies by topics / Multi-Modal representations / Dissertations / Theses

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Dissertations / Theses on the topic 'Multi-Modal representations'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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Consult the top 21 dissertations / theses for your research on the topic 'Multi-Modal representations.'

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1

Gu, Jian. "Multi-modal Neural Representations for Semantic Code Search." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279101.

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In recent decades, various software systems have gradually become the basis of our society. Programmers search existing code snippets from time to time in their daily life. It would be beneficial and meaningful to have better solutions for the task of semantic code search, which is to find the most semantically relevant code snippets for a given query. Our approach is to introduce tree representations by multi-modal learning. The core idea is to enrich semantic information for code snippets by preparing data of different modalities, and meanwhile ignore syntactic information. We design one nov

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2

Liu, Yahui. "Exploring Multi-Domain and Multi-Modal Representations for Unsupervised Image-to-Image Translation." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/342634.

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Unsupervised image-to-image translation (UNIT) is a challenging task in the image manipulation field, where input images in a visual domain are mapped into another domain with desired visual patterns (also called styles). An ideal direction in this field is to build a model that can map an input image in a domain to multiple target domains and generate diverse outputs in each target domain, which is termed as multi-domain and multi-modal unsupervised image-to-image translation (MMUIT). Recent studies have shown remarkable results in UNIT but they suffer from four main limitations: (1) State-of

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3

Song, Pingfan. "Multi-modal image processing via joint sparse representations induced by coupled dictionaries." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10061963/.

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Real-world image processing tasks often involve various image modalities captured by different sensors. However, given that different sensors exhibit different characteristics, such multi-modal images are typically acquired with different resolutions, different blurring kernels, or even noise levels. In view of the fact that images associated with the same scene share some attributes, such as edges, textures or other primitives, it is natural to ask whether one can improve standard image processing tasks by leveraging the availability of multimodal images. This thesis introduces a sparsity-bas

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4

Suthana, Nanthia Ananda. "Investigating human medical temporal representations of episodic information a multi-modal approach /." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1905692921&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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5

Atienza, Nicolas. "Towards Reliable ML : Leveraging Multi-Modal Representations, Information Bottleneck and Extreme Value Theory." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG025.

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Cette thèse de doctorat porte sur l'amélioration de la fiabilité de l'apprentissage automatique, en particulier pour les applications à forts enjeux. Les modèles d'apprentissage profond actuels, bien que très performants, restent difficiles à appréhender et à déployer de manière sûre en raison de leur opacité, de leur vulnérabilité aux attaques adverses, de leur sensibilité aux changements de distribution, et de leur inefficacité en contexte de données ou de ressources limitées. Pour surmonter ces limites, ce travail explore trois dimensions complémentaires : l'explicabilité, la robustesse et

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6

Tran, Thi Quynh Nhi. "Robust and comprehensive joint image-text representations." Thesis, Paris, CNAM, 2017. http://www.theses.fr/2017CNAM1096/document.

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La présente thèse étudie la modélisation conjointe des contenus visuels et textuels extraits à partir des documents multimédias pour résoudre les problèmes intermodaux. Ces tâches exigent la capacité de ``traduire'' l'information d'une modalité vers une autre. Un espace de représentation commun, par exemple obtenu par l'Analyse Canonique des Corrélation ou son extension kernelisée est une solution généralement adoptée. Sur cet espace, images et texte peuvent être représentés par des vecteurs de même type sur lesquels la comparaison intermodale peut se faire directement.Néanmoins, un tel espace

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7

Tran, Thi Quynh Nhi. "Robust and comprehensive joint image-text representations." Electronic Thesis or Diss., Paris, CNAM, 2017. http://www.theses.fr/2017CNAM1096.

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La présente thèse étudie la modélisation conjointe des contenus visuels et textuels extraits à partir des documents multimédias pour résoudre les problèmes intermodaux. Ces tâches exigent la capacité de ``traduire'' l'information d'une modalité vers une autre. Un espace de représentation commun, par exemple obtenu par l'Analyse Canonique des Corrélation ou son extension kernelisée est une solution généralement adoptée. Sur cet espace, images et texte peuvent être représentés par des vecteurs de même type sur lesquels la comparaison intermodale peut se faire directement.Néanmoins, un tel espace

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8

Ben-Younes, Hedi. "Multi-modal representation learning towards visual reasoning." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS173.

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La quantité d'images présentes sur internet augmente considérablement, et il est nécessaire de développer des techniques permettant le traitement automatique de ces contenus. Alors que les méthodes de reconnaissance visuelle sont de plus en plus évoluées, la communauté scientifique s'intéresse désormais à des systèmes aux capacités de raisonnement plus poussées. Dans cette thèse, nous nous intéressons au Visual Question Answering (VQA), qui consiste en la conception de systèmes capables de répondre à une question portant sur une image. Classiquement, ces architectures sont conçues comme des sy

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9

Li, Lin. "Multi-scale spectral embedding representation registration (MSERg) for multi-modal imaging registration." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1467902012.

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10

Gay, Joanna. "Structural representation models for multi-modal image registration in biomedical applications." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-410820.

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In clinical applications it is often beneficial to use multiple imaging technologies to obtain information about different biomedical aspects of the subject under investigation, and to make best use of such sets of images they need to first be registered or aligned. Registration of multi-modal images is a challenging task and is currently the topic of much research, with new methods being published frequently. Structural representation models extract underlying features such as edges from images, distilling them into a common format that can be easily compared across different image modalities

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11

Aissa, Wafa. "Réseaux de modules neuronaux pour un raisonnement visuel compositionnel." Electronic Thesis or Diss., Paris, HESAM, 2023. http://www.theses.fr/2023HESAC033.

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Cette thèse de doctorat porte sur le raisonnement visuel compositionnel. Lorsqu'on présente une paire image-question à un modèle de réseau de neurones, notre objectif est que le modèle réponde à la question en suivant une chaîne de raisonnement définie par un programme. Nous évaluons la capacité de raisonnement du modèle dans le cadre de la Question Réponse Visuelle (QRV). La QRV compositionnelle décompose les questions complexes en sous-problèmes modulaires plus simples. Ces sous-problèmes incluent des compétences de raisonnement telles que la détection d'objets et d'attributs, la détection d

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12

Xu, Dan. "Exploring Multi-Modal and Structured Representation Learning for Visual Image and Video Understanding." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/367610.

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As the explosive growth of the visual data, it is particularly important to develop intelligent visual understanding techniques for dealing with a large amount of data. Many efforts have been made in recent years to build highly effective and large-scale visual processing algorithms and systems. One of the core aspects in the research line is how to learn robust representations to better describe the data. In this thesis we study the problem of visual image and video understanding and specifically, we address the problem via designing and implementing novel multi-modal and structured represent

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13

Xu, Dan. "Exploring Multi-Modal and Structured Representation Learning for Visual Image and Video Understanding." Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2918/1/disclaimer.pdf.

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As the explosive growth of the visual data, it is particularly important to develop intelligent visual understanding techniques for dealing with a large amount of data. Many efforts have been made in recent years to build highly effective and large-scale visual processing algorithms and systems. One of the core aspects in the research line is how to learn robust representations to better describe the data. In this thesis we study the problem of visual image and video understanding and specifically, we address the problem via designing and implementing novel multi-modal and structured represent

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14

Steiling, David. "Icon, representation and virtuality in reading the graphic narrative." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001818.

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15

Siless, Viviana. "Multi-modal registration of T1 brain image and geometric descriptors of white matter tracts." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112147/document.

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Le recalage des images du cerveau vise à réduire la variabilité anatomique entre les differentes sujets, et à créer un espace commun pour l'analyse de groupe. Les approches multi-modales essaient de minimiser les variations de forme du cortex et des structures internes telles que des faisceaux de fibres nerveuses. Ces approches nécessitent une identification préalable de ces structures, ce qui s'avère une tâche difficile en l'absence d'un atlas complet de référence. Nous proposons une extension de l'algorithme de recalage difféomorphe des Démons pour recaler conjointement des images et des fai

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16

Soto-Iglesias, David. "Development and evaluation of mapping strategies for the integration and joint analysis of multi-modal data of the heart." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/395191.

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The development of novel technologies is allowing a complete description of the heart structure and function, including geometrical, myocardial tissue viability and electrical activation information. The joint analysis of this information helps to improve clinical interventions such as radio-frequency ablation of cardiac arrhythmias. However, the acquired data and related indices need to be integrated onto a common reference space for its analysis. This integration is not straightforward due to the different characteristics of acquisition systems. The aim of this thesis was to develop and eva

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17

Huang, Di. "Robust face recognition based on three dimensional data." Phd thesis, Ecole Centrale de Lyon, 2011. http://tel.archives-ouvertes.fr/tel-00693158.

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The face is one of the best biometrics for person identification and verification related applications, because it is natural, non-intrusive, and socially weIl accepted. Unfortunately, an human faces are similar to each other and hence offer low distinctiveness as compared with other biometrics, e.g., fingerprints and irises. Furthermore, when employing facial texture images, intra-class variations due to factors as diverse as illumination and pose changes are usually greater than inter-class ones, making 2D face recognition far from reliable in the real condition. Recently, 3D face data have

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18

Po, Ming Jack. "Multi-scale Representations for Classification of Protein Crystal Images and Multi-Modal Registration of the Lung." Thesis, 2015. https://doi.org/10.7916/D87M06MZ.

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In recent years, multi-resolution techniques have become increasingly popular in the image processing community. New techniques have been developed with applications ranging from edge detection, texture recognition, image registration, multi-resolution features for image classification and more. The central focus of this two-part thesis is the multi-resolution analysis of images. In the first part, we used multi-resolution approaches to help with the classification of a set of protein crystal images. In the second, similar approaches were used to help register a set of 3D image volumes that wo

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19

Weiss, Martin. "Deep reinforcement learning for multi-modal embodied navigation." Thesis, 2020. http://hdl.handle.net/1866/25106.

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Ce travail se concentre sur une tâche de micro-navigation en plein air où le but est de naviguer vers une adresse de rue spécifiée en utilisant plusieurs modalités (par exemple, images, texte de scène et GPS). La tâche de micro-navigation extérieure s’avère etre un défi important pour de nombreuses personnes malvoyantes, ce que nous démontrons à travers des entretiens et des études de marché, et nous limitons notre définition des problèmes à leurs besoins. Nous expérimentons d’abord avec un monde en grille partiellement observable (Grid-Street et Grid City) contenant des maisons, des num

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20

Sylvain, Tristan. "Locality and compositionality in representation learning for complex visual tasks." Thesis, 2021. http://hdl.handle.net/1866/25594.

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L'utilisation d'architectures neuronales profondes associée à des innovations spécifiques telles que les méthodes adversarielles, l’entraînement préalable sur de grands ensembles de données et l'estimation de l'information mutuelle a permis, ces dernières années, de progresser rapidement dans de nombreuses tâches de vision par ordinateur complexes telles que la classification d'images de catégories préalablement inconnues (apprentissage zéro-coups), la génération de scènes ou la classification multimodale. Malgré ces progrès, il n’est pas certain que les méthodes actuelles d’apprentissage de r

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21

"Representing and Reasoning about Dynamic Multi-Agent Domains: An Action Language Approach." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.49093.

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abstract: Reasoning about actions forms the basis of many tasks such as prediction, planning, and diagnosis in a dynamic domain. Within the reasoning about actions community, a broad class of languages, called action languages, has been developed together with a methodology for their use in representing and reasoning about dynamic domains. With a few notable exceptions, the focus of these efforts has largely centered around single-agent systems. Agents rarely operate in a vacuum however, and almost in parallel, substantial work has been done within the dynamic epistemic logic community towards

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