Relevant bibliographies by topics / Multi-Modal representations

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

Academic literature on the topic 'Multi-Modal representations'

Author: Grafiati
Published: 7 July 2024
Last updated: 31 July 2025

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Journal articles on the topic "Multi-Modal representations"

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Wu, Lianlong, Seewon Choi, Daniel Raggi, et al. "Generation of Visual Representations for Multi-Modal Mathematical Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23850–52. http://dx.doi.org/10.1609/aaai.v38i21.30586.

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In this paper we introduce MaRE, a tool designed to generate representations in multiple modalities for a given mathematical problem while ensuring the correctness and interpretability of the transformations between different representations. The theoretical foundation for this tool is Representational Systems Theory (RST), a mathematical framework for studying the structure and transformations of representations. In MaRE’s web front-end user interface, a set of probability equations in Bayesian Notation can be rigorously transformed into Area Diagrams, Contingency Tables, and Probability Tree
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Zhang, Yi, Mingyuan Chen, Jundong Shen, and Chongjun Wang. "Tailor Versatile Multi-Modal Learning for Multi-Label Emotion Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 9100–9108. http://dx.doi.org/10.1609/aaai.v36i8.20895.

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Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various human emotions from heterogeneous visual, audio and text modalities. Previous methods mainly focus on projecting multiple modalities into a common latent space and learning an identical representation for all labels, which neglects the diversity of each modality and fails to capture richer semantic information for each label from different perspectives. Besides, associated relationships of modalities and labels have not been fully exploited. In this paper, we propose versaTile multi-modAl learning for multI-labeL emOti
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Zhang, Yichi, Zhuo Chen, Lingbing Guo, et al. "Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13322–30. https://doi.org/10.1609/aaai.v39i12.33454.

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Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given multi-modal knowledge graphs (MMKG), collaboratively leveraging structural information from the triples and multi-modal information of the entities to overcome the inherent incompleteness. Existing MMKGC methods usually extract multi-modal features with pre-trained models and employ fusion modules to integrate multi-modal features for the entities. This often results in coarse handling of multi-modal entity information, overlooking the nuanced, fine-grained semantic details and their complex interac
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Dixitha, Bandi. "Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49325.

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Abstract - In this paper, we propose a novel Multi-Stage Multi-Modal Pre-Training framework for Automatic Speech Recognition (ASR) that effectively leverages the complementary information from multiple modalities, such as audio, text, and visual context, to enhance model performance. Our approach consists of three sequential pre-training stages: (1) a Masked Audio Encoding (MAE) stage that learns robust acoustic representations by reconstructing masked segments of speech, (2) a Cross-Modal Learning Regularization (CLR) stage that aligns acoustic and visual-textual representations using a contr
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Zhang, Dong, Suzhong Wei, Shoushan Li, Hanqian Wu, Qiaoming Zhu, and Guodong Zhou. "Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14347–55. http://dx.doi.org/10.1609/aaai.v35i16.17687.

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Multi-modal named entity recognition (MNER) aims to discover named entities in free text and classify them into pre-defined types with images. However, dominant MNER models do not fully exploit fine-grained semantic correspondences between semantic units of different modalities, which have the potential to refine multi-modal representation learning. To deal with this issue, we propose a unified multi-modal graph fusion (UMGF) approach for MNER. Specifically, we first represent the input sentence and image using a unified multi-modal graph, which captures various semantic relationships between
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Liu, Hao, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu, and Hui Xiong. "Multi-modal transportation recommendation with unified route representation learning." Proceedings of the VLDB Endowment 14, no. 3 (2020): 342–50. http://dx.doi.org/10.14778/3430915.3430924.

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Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learni
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Wang, Huansha, Qinrang Liu, Ruiyang Huang, and Jianpeng Zhang. "Multi-Modal Entity Alignment Method Based on Feature Enhancement." Applied Sciences 13, no. 11 (2023): 6747. http://dx.doi.org/10.3390/app13116747.

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Multi-modal entity alignment refers to identifying equivalent entities between two different multi-modal knowledge graphs that consist of multi-modal information such as structural triples and descriptive images. Most previous multi-modal entity alignment methods have mainly used corresponding encoders of each modality to encode entity information and then perform feature fusion to obtain the multi-modal joint representation. However, this approach does not fully utilize the multi-modal information of aligned entities. To address this issue, we propose MEAFE, a multi-modal entity alignment met
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Hu, Shizhe, Jiahao Fan, Guoliang Zou, and Yangdong Ye. "Multi-aspect Self-guided Deep Information Bottleneck for Multi-modal Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 17314–22. https://doi.org/10.1609/aaai.v39i16.33903.

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Deep multi-modal clustering can extract useful information among modals, thus benefiting the final clustering and many related fields. However, existing multi-modal clustering methods have two major limitations. First, they often ignore different levels of guiding information from both the feature representations and cluster assignments, which thus are difficult in learning discriminative representations. Second, most methods fail to effectively eliminate redundant information between multi-modal data, negatively affecting clustering results. In this paper, we propose a novel multi-aspect self
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Wu, Tianxing, Chaoyu Gao, Lin Li, and Yuxiang Wang. "Leveraging Multi-Modal Information for Cross-Lingual Entity Matching across Knowledge Graphs." Applied Sciences 12, no. 19 (2022): 10107. http://dx.doi.org/10.3390/app121910107.

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In recent years, the scale of knowledge graphs and the number of entities have grown rapidly. Entity matching across different knowledge graphs has become an urgent problem to be solved for knowledge fusion. With the importance of entity matching being increasingly evident, the use of representation learning technologies to find matched entities has attracted extensive attention due to the computability of vector representations. However, existing studies on representation learning technologies cannot make full use of knowledge graph relevant multi-modal information. In this paper, we propose
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Sun, Shuoji, Miao Yu, and Xu Yu. "Diversified Interpretable Compatibility Modeling Based on Multi-modal Disentanglement." Applied and Computational Engineering 163, no. 1 (2025): 66–78. https://doi.org/10.54254/2755-2721/2025.24500.

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In recent years, compatibility modeling for evaluating whether fashion items match has received widespread attention. The existing compatibility modeling methods typically model the compatibility between fashion items based on multi-modal information. However, these methods often fail to disentangle the rich attribute information in the high-dimensional continuous representations of items, resulting in a lack of interpretability in recommendations. At the same time, they also overlook the diverse matching methods among the attributes of complementary items. This article proposes a Diversified
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Dissertations / Theses on the topic "Multi-Modal representations"

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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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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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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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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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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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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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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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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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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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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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Books on the topic "Multi-Modal representations"

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Po, Ming Jack. Multi-scale Representations for Classification of Protein Crystal Images and Multi-Modal Registration of the Lung. [publisher not identified], 2015.

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(Editor), Syed A. Ali, and Susan McRoy (Editor), eds. Representations for Multi-Modal Human-Computer Interaction: Papers from the Aaai Workshop (Technical Reports Vol. Ws-98-09). AAAI Press, 1998.

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Case, Julialicia, Eric Freeze, and Salvatore Pane. Story Mode. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9781350301405.

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Against the backdrop of a hyper-competitive AAA industry and the perception that it is a world reserved for top programmers and hard-core ‘gamers’, Story Mode offers an accessible entry-point for all into writing and designing complex and emotionally affecting narrative video games. The first textbook to combine game design with creative writing techniques, this much-needed resource makes the skills necessary to consume and create digital and multi-modal stories attainable and fun. Appealing to the growing calls for greater inclusivity and access to this important contemporary apparatus of exp
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Book chapters on the topic "Multi-Modal representations"

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Wiesen, Aryeh, and Yaakov HaCohen-Kerner. "Overview of Uni-modal and Multi-modal Representations for Classification Tasks." In Natural Language Processing and Information Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91947-8_41.

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Li, Cheng, Hui Sun, Zaiyi Liu, Meiyun Wang, Hairong Zheng, and Shanshan Wang. "Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32245-8_7.

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Luo, Xi, Chunjie Cao, and Longjuan Wang. "Multi-modal Universal Embedding Representations for Language Understanding." In Communications in Computer and Information Science. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0523-0_7.

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Zhao, Xiang, Weixin Zeng, and Jiuyang Tang. "Multimodal Entity Alignment." In Entity Alignment. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4250-3_9.

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AbstractIn various tasks related to artificial intelligence, data is often present in multiple forms or modalities. Recently, it has become a popular approach to combine these different forms of information into a knowledge graph, creating a multi-modal knowledge graph (MMKG). However, multi-modal knowledge graphs (MMKGs) often face issues of insufficient data coverage and incompleteness. In order to address this issue, a possible strategy is to incorporate supplemental information from other multi-modal knowledge graphs (MMKGs). To achieve this goal, current methods for aligning entities coul
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Bae, Inhwan, Jin-Hwi Park, and Hae-Gon Jeon. "Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20047-2_16.

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Florea, Filip, Alexandrina Rogozan, Eugen Barbu, Abdelaziz Bensrhair, and Stefan Darmoni. "MedIC at ImageCLEF 2006: Automatic Image Categorization and Annotation Using Combined Visual Representations." In Evaluation of Multilingual and Multi-modal Information Retrieval. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74999-8_82.

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Qin, Chen, Bibo Shi, Rui Liao, Tommaso Mansi, Daniel Rueckert, and Ali Kamen. "Unsupervised Deformable Registration for Multi-modal Images via Disentangled Representations." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20351-1_19.

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Ge, Hongkun, Guorong Wu, Li Wang, Yaozong Gao, and Dinggang Shen. "Hierarchical Multi-modal Image Registration by Learning Common Feature Representations." In Machine Learning in Medical Imaging. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24888-2_25.

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Dorent, Reuben, Nazim Haouchine, Fryderyk Kogl, et al. "Unified Brain MR-Ultrasound Synthesis Using Multi-modal Hierarchical Representations." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43999-5_43.

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Kasiri, Keyvan, Paul Fieguth, and David A. Clausi. "Structural Representations for Multi-modal Image Registration Based on Modified Entropy." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20801-5_9.

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Conference papers on the topic "Multi-Modal representations"

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You, Chenyu, Yifei Mint, Weicheng Dai, Jasjeet S. Sekhon, Lawrence Staib, and James S. Duncan. "Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02470.

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Houdré, Nicolas, Diego Marcos, Dino Ienco, Laurent Wendling, Camille Kurtz, and Sylvain Lobry. "ProMM-RS: Exploring Probabilistic Learning for Multi-Modal Remote Sensing Image Representations." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW). IEEE, 2025. https://doi.org/10.1109/wacvw65960.2025.00063.

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Nakagi, Yuko, Takuya Matsuyama, Naoko Koide-Majima, et al. "Unveiling Multi-level and Multi-modal Semantic Representations in the Human Brain using Large Language Models." 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.1133.

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Yang, Kaixiang, Wenqi Shan, Xudong Li, et al. "Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor Segmentation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822635.

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Chi, Hyung-Gun, Jose Barreiros, Jean Mercat, Karthik Ramani, and Thomas Kollar. "Multi-Modal Representation Learning with Tactile Data." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802699.

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Mu, Hongzhang, Shuili Zhang, Quangang Li, Tingwen Liu, and Hongbo Xu. "Dynamic Multi-Modal Representation Learning For Topic Modeling." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10688179.

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Sun, Yu, Xian Fu, Zhuzhu Zhang, Ningning Zhang, Hui Zhang, and Yaqiang Cao. "Multi-Modal Fake News Detection Aided by Multi-Viewpoint Representation from a Multi-Modal Large Language Model." In 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 2024. https://doi.org/10.1109/wi-iat62293.2024.00129.

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Artale, Alessandro, Roman Kontchakov, Andrea Mazzullo, and Frank Wolter. "Non-Rigid Designators in Modal and Temporal Free Description Logics." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/8.

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Definite descriptions, such as ‘the General Chair of KR 2024’, are a semantically transparent device for object identification in knowledge representation. In first-order modal logic, definite descriptions have been widely investigated for their non-rigidity, which allows them to designate different objects (or none at all) at different states. We propose expressive modal description logics with non-rigid definite descriptions and names, and investigate decidability and complexity of the satisfiability problem. We first systematically link satisfiability for the one-variable fragment of first-
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Zolfaghari, Mohammadreza, Yi Zhu, Peter Gehler, and Thomas Brox. "CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00148.

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Lee, O.-Joun, and Jin-Taek Kim. "Learning Multi-modal Representations of Narrative Multimedia." In RACS '20: International Conference on Research in Adaptive and Convergent Systems. ACM, 2020. http://dx.doi.org/10.1145/3400286.3418216.

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