Relevant bibliographies by topics / Scalability in Cross-Modal Retrieval

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Academic literature on the topic 'Scalability in Cross-Modal Retrieval'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Scalability in Cross-Modal Retrieval"

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Yang, Bo, Chen Wang, Xiaoshuang Ma, Beiping Song, Zhuang Liu, and Fangde Sun. "Zero-Shot Sketch-Based Remote-Sensing Image Retrieval Based on Multi-Level and Attention-Guided Tokenization." Remote Sensing 16, no. 10 (2024): 1653. http://dx.doi.org/10.3390/rs16101653.

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Effectively and efficiently retrieving images from remote-sensing databases is a critical challenge in the realm of remote-sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the potential of multi-level feature integration from sketches remains underexplored, leading to suboptimal retrieval performance. To address this gap, our study introduces a novel zero-shot, sketch-based retrieval method for remote-sensing images, leveraging multi-level feature extraction, self-attention-guided tokenization and filtering, and cross-modali
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Hu, Peng, Hongyuan Zhu, Xi Peng, and Jie Lin. "Semi-Supervised Multi-Modal Learning with Balanced Spectral Decomposition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 99–106. http://dx.doi.org/10.1609/aaai.v34i01.5339.

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Cross-modal retrieval aims to retrieve the relevant samples across different modalities, of which the key problem is how to model the correlations among different modalities while narrowing the large heterogeneous gap. In this paper, we propose a Semi-supervised Multimodal Learning Network method (SMLN) which correlates different modalities by capturing the intrinsic structure and discriminative correlation of the multimedia data. To be specific, the labeled and unlabeled data are used to construct a similarity matrix which integrates the cross-modal correlation, discrimination, and intra-moda
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Rasheed, Ali Salim, Davood Zabihzadeh, and Sumia Abdulhussien Razooqi Al-Obaidi. "Large-Scale Multi-modal Distance Metric Learning with Application to Content-Based Information Retrieval and Image Classification." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 13 (2020): 2050034. http://dx.doi.org/10.1142/s0218001420500342.

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Metric learning algorithms aim to make the conceptually related data items closer and keep dissimilar ones at a distance. The most common approach for metric learning on the Mahalanobis method. Despite its success, this method is limited to find a linear projection and also suffer from scalability respecting both the dimensionality and the size of input data. To address these problems, this paper presents a new scalable metric learning algorithm for multi-modal data. Our method learns an optimal metric for any feature set of the multi-modal data in an online fashion. We also combine the learne
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Popov, S. E., V. P. Potapov, and R. Y. Zamaraev. "On an Approach to Developing Information and Reference Systems Based on Large Language Models." Vestnik NSU. Series: Information Technologies 23, no. 1 (2025): 46–66. https://doi.org/10.25205/1818-7900-2025-23-1-46-66.

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The aim of this study is to develop a corporate context-aware question-answering system in the form of a chatbot to support territorial management by providing fast and accurate access to relevant information. The system is built upon large language models leveraging the Retrieval-Augmented Generation (RAG) approach, combined with modern data processing and retrieval techniques. The knowledge base of the system incorporates textual and tabular data extracted from official reports on environmental conditions. A PostgreSQL database with the pgvector extension was employed to store and retrieve l
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Zalkow, Frank, and Meinard Müller. "Learning Low-Dimensional Embeddings of Audio Shingles for Cross-Version Retrieval of Classical Music." Applied Sciences 10, no. 1 (2019): 19. http://dx.doi.org/10.3390/app10010019.

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Cross-version music retrieval aims at identifying all versions of a given piece of music using a short query audio fragment. One previous approach, which is particularly suited for Western classical music, is based on a nearest neighbor search using short sequences of chroma features, also referred to as audio shingles. From the viewpoint of efficiency, indexing and dimensionality reduction are important aspects. In this paper, we extend previous work by adapting two embedding techniques; one is based on classical principle component analysis, and the other is based on neural networks with tri
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Huang, Xiaobing, Tian Zhao, and Yu Cao. "PIR." International Journal of Multimedia Data Engineering and Management 5, no. 3 (2014): 1–27. http://dx.doi.org/10.4018/ijmdem.2014070101.

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Multimedia Information Retrieval (MIR) is a problem domain that includes programming tasks such as salient feature extraction, machine learning, indexing, and retrieval. There are a variety of implementations and algorithms for these tasks in different languages and frameworks, which are difficult to compose and reuse due to the interface and language incompatibility. Due to this low reusability, researchers often have to implement their experiments from scratch and the resulting programs cannot be easily adapted to parallel and distributed executions, which is important for handling large dat
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An, Duo, Alan Chiu, James A. Flanders, et al. "Designing a retrievable and scalable cell encapsulation device for potential treatment of type 1 diabetes." Proceedings of the National Academy of Sciences 115, no. 2 (2017): E263—E272. http://dx.doi.org/10.1073/pnas.1708806115.

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Cell encapsulation has been shown to hold promise for effective, long-term treatment of type 1 diabetes (T1D). However, challenges remain for its clinical applications. For example, there is an unmet need for an encapsulation system that is capable of delivering sufficient cell mass while still allowing convenient retrieval or replacement. Here, we report a simple cell encapsulation design that is readily scalable and conveniently retrievable. The key to this design was to engineer a highly wettable, Ca2+-releasing nanoporous polymer thread that promoted uniform in situ cross-linking and stron
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Zhang, Zhen, Xu Wu, and Shuang Wei. "Cross-Domain Access Control Model in Industrial IoT Environment." Applied Sciences 13, no. 8 (2023): 5042. http://dx.doi.org/10.3390/app13085042.

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The Industrial Internet of Things (IIoT) accelerates smart manufacturing and boosts production efficiency through heterogeneous industrial equipment, intelligent sensors, and actuators. The Industrial Internet of Things is transforming from a traditional factory model to a new manufacturing mode, which allows cross-domain data-sharing among multiple system departments to enable smart manufacturing. A complete industrial product comes from the combined efforts of many different departments. Therefore, secure and reliable cross-domain access control has become the key to ensuring the security of
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Ievgen, Gartman. "Architectural Features of Extended Retrieval Generation with External Memory." International Journal of Engineering and Computer Science 14, no. 06 (2025): 27355–61. https://doi.org/10.18535/ijecs.v14i06.5163.

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This article examines the RoCR framework, a Retrieval-Augmented Generation (RAG) system optimized for edge deployment in latency-sensitive environments such as real-time search, product recommendation, and dynamic content generation in eCommerce platforms. RoCR leverages Compute-in-Memory (CiM) architectures to enable fast, energy-efficient inference at scale. At the core of the solution is the CiM-Retriever, a module optimized for performing max inner product search (MIPS). Two architectural variants of the generator are analyzed—decoder-only (RA-T) and encoder–decoder with kNN cross-attentio
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DS, Chakrapani. "AN EFFICIENT DATA SECURITY IN MEDICAL REPORT USING BLOCKCHAIN TECHNOLOGY." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34688.

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The 'SwiftApply Assistant' addresses the common challenge of time consuming data entry across a multitude of applications. This innovative tool simplifies interactions with forms such as job applications, admissions, claims, scholarships, and healthcare enrollment by automating the process of filling HTML forms. Noteworthy features include its adaptability to various life stages and a user centric design aimed at streamlining the application process while offering control and customization options. With cross platform accessibility ensuring convenience and scalability to adapt to emerging appl
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Dissertations / Theses on the topic "Scalability in Cross-Modal Retrieval"

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Shen, Yuming. "Deep binary representation learning for single/cross-modal data retrieval." Thesis, University of East Anglia, 2018. https://ueaeprints.uea.ac.uk/67635/.

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Data similarity search is widely regarded as a classic topic in the realms of computer vision, machine learning and data mining. Providing a certain query, the retrieval model sorts out the related candidates in the database according to their similarities, where representation learning methods and nearest-neighbour search apply. As matching data features in Hamming space is computationally cheaper than in Euclidean space, learning to hash and binary representations are generally appreciated in modern retrieval models. Recent research seeks solutions in deep learning to formulate the hash func
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Zhu, Meng. "Cross-modal semantic-associative labelling, indexing and retrieval of multimodal data." Thesis, University of Reading, 2010. http://centaur.reading.ac.uk/24828/.

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Saragiotis, Panagiotis. "Cross-modal classification and retrieval of multimodal data using combinations of neural networks." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/843338/.

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Current neurobiological thinking supported, in part, by experimentation stresses the importance of cross-modality. Uni-modal cognitive tasks, language and vision, for example, are performed with the help of many networks working simultaneously or sequentially; and for cross-modal tasks, like picture / object naming and word illustration, the output of these networks is combined to produce higher cognitive behaviour. The notion of multi-net processing is used typically in the pattern recognition literature, where ensemble networks of weak classifiers - typically supervised - appear to outperfor
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Surian, Didi. "Novel Applications Using Latent Variable Models." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14014.

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Latent variable models have achieved a great success in many research communities, including machine learning, information retrieval, data mining, natural language processing, etc. Latent variable models use an assumption that the data, which is observable, has an affinity to some hidden/latent variables. In this thesis, we present a suite of novel applications using latent variable models. In particular, we (i) extend topic models using directional distributions, (ii) propose novel solutions using latent variable models to detect outliers (anomalies) and (iii) to answer cross-modal retrieval
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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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Mandal, Devraj. "Cross-Modal Retrieval and Hashing." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4685.

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The objective of cross-modal retrieval is to retrieve relevant items from one modality (say image), given a query from another modality (say textual document). Cross-modal retrieval has various applications like matching image-sketch, audio-visual, near infrared-RGB, etc. Different feature representations of the two modalities, absence of paired correspondences, etc. makes this a very challenging problem. In this thesis, we have extensively looked at the cross-modal retrieval problem from different aspects and proposed methodologies to address them. • In the first work, we propose a novel f
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Li, Yan-Fu, and 李彥甫. "The Cross-Modal Method of Tag Labeling in Music Information Retrieval." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/45038305568580924323.

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碩士<br>輔仁大學<br>資訊工程學系<br>96<br>A music object contains multi-facet feature, such as the average frequency, speed, timbre, melody, rhythm, genre and so on. We conclude that these features are extracted from various feature domains, respectively. Moreover, these feature do- mains are separated into two types, the quantified and the unquantifiable. Within the quantified feature domain, the features are quantified as the numerical value, for example, if there are three important average frequencies in a music object, we quantify and denote as three numerical values: 20Hz, 80Hz and 100Hz in the feat
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Yang, Bo. "Semantic-aware data processing towards cross-modal multimedia analysis and content-based retrieval in distributed and mobile environments /." 2007. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-1850/index.html.

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Ramanishka, Vasili. "Describing and retrieving visual content using natural language." Thesis, 2020. https://hdl.handle.net/2144/42026.

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Modern deep learning methods have boosted research progress in visual recognition and text understanding but it is a non-trivial task to unite these advances from both disciplines. In this thesis, we develop models and techniques that allow us to connect natural language and visual content enabling automatic video subtitling, visual grounding, and text-based image search. Such models could be useful in a wide range of applications in robotics and human-computer interaction bridging the gap in vision and language understanding. First, we develop a model that generates natural language descr
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Books on the topic "Scalability in Cross-Modal Retrieval"

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C, Peters, ed. Evaluation of multilingual and multi-modal information retrieval: 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Alicante, Spain, September 20-22, 2006 ; revised selected papers. Springer, 2007.

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Gey, Fredric C., Paul Clough, Bernardo Magnini, Douglas W. Oard, and Jussi Karlgren. Evaluation of Multilingual and Multi-Modal Information Retrieval: 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Alicante, Spain, September 20-22, 2006, Revised Selected Papers. Springer London, Limited, 2007.

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Book chapters on the topic "Scalability in Cross-Modal Retrieval"

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Zhu, Lei, Jingjing Li, and Weili Guan. "Cross-Modal Hashing." In Synthesis Lectures on Information Concepts, Retrieval, and Services. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-37291-9_3.

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Li, Qing, and Yu Yang. "Cross-Modal Multimedia Information Retrieval." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_90-2.

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Li, Qing, and Yu Yang. "Cross-Modal Multimedia Information Retrieval." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_90.

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Wen, Zhenyu, and Aimin Feng. "Deep Centralized Cross-modal Retrieval." In MultiMedia Modeling. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67832-6_36.

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Li, Qing, and Yu Yang. "Cross-Modal Multimedia Information Retrieval." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_90.

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Chen, Zhihao, and Hongya Wang. "TSCMR:Two-Stage Cross-Modal Retrieval." In Advanced Data Mining and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46674-8_39.

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Ding, Guohui, Zhonghua Li, and Yongqiang Ren. "Modality-Specific Hashing: Transform Cross-Modal Retrieval Into Single-Modal Retrieval." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-96-2061-6_32.

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Ning, Xuecheng, Xiaoshan Yang, and Changsheng Xu. "Multi-hop Interactive Cross-Modal Retrieval." In MultiMedia Modeling. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37734-2_55.

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Malik, Shaily, Nikhil Bhardwaj, Rahul Bhardwaj, and Saurabh Kumar. "Cross-Modal Retrieval Using Deep Learning." In Proceedings of Third Doctoral Symposium on Computational Intelligence. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3148-2_62.

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Xuan, Ruisheng, Weihua Ou, Quan Zhou, et al. "Semantics Consistent Adversarial Cross-Modal Retrieval." In Cognitive Internet of Things: Frameworks, Tools and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04946-1_45.

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Conference papers on the topic "Scalability in Cross-Modal Retrieval"

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Waltenspul, Raphael, Florian Spiess, and Heiko Schuldt. "Cross-Modal 3D Model Retrieval." In 2024 International Symposium on Multimedia (ISM). IEEE, 2024. https://doi.org/10.1109/ism63611.2024.00039.

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Yin, Hanlei. "Cross-modal Retrieval with Attention Knowledge Distillation." In 2024 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 2024. http://dx.doi.org/10.1109/ipec61310.2024.00078.

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Palma Gomez, Frank, Ramon Sanabria, Yun-hsuan Sung, Daniel Cer, Siddharth Dalmia, and Gustavo Hernandez Abrego. "Transforming LLMs into Cross-modal and Cross-lingual Retrieval Systems." In Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.iwslt-1.4.

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Wang, Bokun, Yang Yang, Xing Xu, Alan Hanjalic, and Heng Tao Shen. "Adversarial Cross-Modal Retrieval." In MM '17: ACM Multimedia Conference. ACM, 2017. http://dx.doi.org/10.1145/3123266.3123326.

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Jing, Longlong, Elahe Vahdani, Jiaxing Tan, and Yingli Tian. "Cross-Modal Center Loss for 3D Cross-Modal Retrieval." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00316.

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Zhen, Liangli, Peng Hu, Xu Wang, and Dezhong Peng. "Deep Supervised Cross-Modal Retrieval." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01064.

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Ranjan, Viresh, Nikhil Rasiwasia, and C. V. Jawahar. "Multi-label Cross-Modal Retrieval." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.466.

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Zong, Linlin, Qiujie Xie, Jiahui Zhou, Peiran Wu, Xianchao Zhang, and Bo Xu. "FedCMR: Federated Cross-Modal Retrieval." In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2021. http://dx.doi.org/10.1145/3404835.3462989.

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Hou, Danyang, Liang Pang, Yanyan Lan, Huawei Shen, and Xueqi Cheng. "Region-based Cross-modal Retrieval." In 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. http://dx.doi.org/10.1109/ijcnn55064.2022.9892139.

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Xu, Xing, Fumin Shen, Yang Yang, and Heng Tao Shen. "Discriminant Cross-modal Hashing." In ICMR'16: International Conference on Multimedia Retrieval. ACM, 2016. http://dx.doi.org/10.1145/2911996.2912056.

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