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Relevant bibliographies by topics / Cross-modal document classification

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Journal articles
Dissertations / Theses
Conference papers

Academic literature on the topic 'Cross-modal document classification'

Author: Grafiati

Published: 7 September 2023

Last updated: 31 July 2025

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Journal articles on the topic "Cross-modal document classification"

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Zeng, Dehong, Xiaosong Chen, Zhengxin Song, Yun Xue, and Qianhua Cai. "Multimodal Interaction and Fused Graph Convolution Network for Sentiment Classification of Online Reviews." Mathematics 11, no. 10 (2023): 2335. http://dx.doi.org/10.3390/math11102335.

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An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on multimodal data becomes a major focus. In this work, we propose a novel Multimodal Interactive and Fusion Graph Convolutional Network to deal with both texts and images on the task of document-level multimodal sentiment analysis. The image caption is introduced as an auxiliary, which is aligned with the image to enhance the semantics delivery. Then, a graph is constructed with the sentences and images generated as nodes. In line with the gra

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2

Okpaleke, Michael Sunday, Adaora Doreen Nwajagu, Ezechukwu uche, Daniel Chimuanya Ugwuanyi, and Michael Promise Ogolodom. "PERCEPTION OF MAMMOGRAPHY EXAMINATION BY MIDDLE-AGED WOMEN IN PUBLIC SCHOOLS IN NNEWI NORTH LOCAL GOVERNMENT AREA, NIGERIA." Nigerian Journal of Medical Imaging and Radiation Therapy 11, no. 1 (2022): 12–21. https://doi.org/10.5281/zenodo.6480429.

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<strong>PERCEPTION OF MAMMOGRAPHY EXAMINATION BY MIDDLE-AGED WOMEN IN PUBLIC SCHOOLS IN NNEWI NORTH LOCAL GOVERNMENT AREA,  ANAMBRA STATE, NIGERIA.</strong>                                                            <strong>OKPALEKE MICHAEL SUNDAY <sup>1 </sup>NWAJAGU ADAORA DOREEN <sup>1</sup>, UCHE EZE

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Liu, Tengfei, Yongli Hu, Junbin Gao, Yanfeng Sun, and Baocai Yin. "Cross-Modal Multiple Granularity Interactive Fusion Network for Long Document Classification." ACM Transactions on Knowledge Discovery from Data, November 6, 2023. http://dx.doi.org/10.1145/3631711.

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Long Document Classification (LDC) has attracted great attention in NLP and achieved considerable progress owing to the large-scale pre-trained language models. In spite of this, as a different problem from the traditional text classification, LDC is far from being settled. Long documents, such as news and articles, generally have more than thousands of words with complex structures. Moreover, compared with flat text, long documents usually contain multi-modal content of images, which provide rich information but not yet being utilized for classification. In this paper, we propose a novel cros

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4

Bakkali, Souhail, Zuheng Ming, Mickael Coustaty, Marçal Rusiñol, and Oriol Ramos Terrades. "VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification." Pattern Recognition, February 2023, 109419. http://dx.doi.org/10.1016/j.patcog.2023.109419.

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Dissertations / Theses on the topic "Cross-modal document classification"

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Bakkali, Souhail. "Multimodal Document Understanding with Unified Vision and Language Cross-Modal Learning." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS046.

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Les modèles développés dans cette thèse sont le résultat d'un processus itératif d'analyse et de synthèse entre les théories existantes et nos études réalisées. Plus spécifiquement, nous souhaitons étudier l'apprentissage inter-modal pour la compréhension contextualisée sur les composants des documents à travers le langage et la vision. Cette thèse porte sur l'avancement de la recherche sur l'apprentissage inter-modal et apporte des contributions sur quatre fronts : (i) proposer une approche inter-modale avec des réseaux profonds pour exploiter conjointement les informations visuelles et textu

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2

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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Conference papers on the topic "Cross-modal document classification"

1

Bakkali, Souhail, Sanket Biswas, Zuheng Ming, et al. "GlobalDoc: A Cross-Modal Vision-Language Framework for Real-World Document Image Retrieval and Classification." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00147.

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Bakkali, Souhail, Zuheng Ming, Mickael Coustaty, and Marcal Rusinol. "Cross-Modal Deep Networks For Document Image Classification." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9191268.

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