Relevant bibliographies by topics / Cross-domain retrieval

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

Academic literature on the topic 'Cross-domain retrieval'

Author: Grafiati
Published: 6 September 2023
Last updated: 26 July 2025

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Journal articles on the topic "Cross-domain retrieval"

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Guo, Aibo, Xinyi Li, Ning Pang, and Xiang Zhao. "Adversarial Cross-domain Community Question Retrieval." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 3 (2022): 1–22. http://dx.doi.org/10.1145/3487291.

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Community Q&A forum is a special type of social media that provides a platform to raise questions and to answer them (both by forum participants), to facilitate online information sharing. Currently, community Q&A forums in professional domains have attracted a large number of users by offering professional knowledge. To support information access and save users’ efforts of raising new questions, they usually come with a question retrieval function, which retrieves similar existing questions (and their answers) to a user’s query. However, it can be difficult for community Q&A forum
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Luo, Bingjun, Jinpeng Wang, Zewen Wang, Junjie Zhu, and Xibin Zhao. "Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset Text-to-Image Person Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 568–76. https://doi.org/10.1609/aaai.v39i1.32037.

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Video surveillance systems are crucial components for ensuring public safety and management in smart city. As a fundamental task in video surveillance, text-to-image person retrieval aims to retrieve the target person from an image gallery that best matches the given text description. Most existing text-to-image person retrieval methods are trained in a supervised manner that requires sufficient labeled data in the target domain. However, it is common in practice that only unlabeled data is available in the target domain due to the difficulty and cost of data annotation, which limits the gener
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Wang, Xu, Dezhong Peng, Ming Yan, and Peng Hu. "Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10200–10208. http://dx.doi.org/10.1609/aaai.v37i8.26215.

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Cross-domain image retrieval aims at retrieving images across different domains to excavate cross-domain classificatory or correspondence relationships. This paper studies a less-touched problem of cross-domain image retrieval, i.e., unsupervised cross-domain image retrieval, considering the following practical assumptions: (i) no correspondence relationship, and (ii) no category annotations. It is challenging to align and bridge distinct domains without cross-domain correspondence. To tackle the challenge, we present a novel Correspondence-free Domain Alignment (CoDA) method to effectively el
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Liu, Haoran, Ying Ma, Ming Yan, Yingke Chen, Dezhong Peng, and Xu Wang. "DiDA: Disambiguated Domain Alignment for Cross-Domain Retrieval with Partial Labels." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3612–20. http://dx.doi.org/10.1609/aaai.v38i4.28150.

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Driven by generative AI and the Internet, there is an increasing availability of a wide variety of images, leading to the significant and popular task of cross-domain image retrieval. To reduce annotation costs and increase performance, this paper focuses on an untouched but challenging problem, i.e., cross-domain image retrieval with partial labels (PCIR). Specifically, PCIR faces great challenges due to the ambiguous supervision signal and the domain gap. To address these challenges, we propose a novel method called disambiguated domain alignment (DiDA) for cross-domain retrieval with partia
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Xu, Bowen, Zhenchang Xing, Xin Xia, David Lo, and Shanping Li. "Domain-specific cross-language relevant question retrieval." Empirical Software Engineering 23, no. 2 (2017): 1084–122. http://dx.doi.org/10.1007/s10664-017-9568-3.

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Li, Qiang, Li Zhuang, Qiulin Wang, Xiaodong Zhang, and Jianghai Chen. "Multi-level and cross-domain search engine based on graph convolutional neural network." International Journal of Low-Carbon Technologies 19 (2024): 1215–21. http://dx.doi.org/10.1093/ijlct/ctae059.

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Abstract With the advent of the information age, there is a growing demand for multi-level cross-domain information retrieval by users. However, current technological solutions have not yet overcome the dilemma of automatic adjustment of search scope in cross-domain retrieval. This article proposes a multi-level cross-domain retrieval engine based on graph convolutional neural networks (GCNs). GCN can effectively capture the complex semantic structures in documents, excavate the potential dynamic context of documents by learning the relationships between nodes in the graph structure, adapt to
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Ikeda, Kanami, Hidenori Suzuki, and Eriko Watanabe. "Optical correlation-based cross-domain image retrieval system." Optics Letters 42, no. 13 (2017): 2603. http://dx.doi.org/10.1364/ol.42.002603.

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Wang, Xinggang, Xiong Duan, and Xiang Bai. "Deep sketch feature for cross-domain image retrieval." Neurocomputing 207 (September 2016): 387–97. http://dx.doi.org/10.1016/j.neucom.2016.04.046.

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Noh, Hae-Chan, and Jae-Pil Heo. "Mutually Orthogonal Softmax Axes for Cross-Domain Retrieval." IEEE Access 8 (2020): 56491–500. http://dx.doi.org/10.1109/access.2020.2982557.

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Yin, Ziniu, Yanglin Feng, Ming Yan, Xiaomin Song, Dezhong Peng, and Xu Wang. "RoDA: Robust Domain Alignment for Cross-Domain Retrieval Against Label Noise." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 9535–43. https://doi.org/10.1609/aaai.v39i9.33033.

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This paper studies the complex challenge of cross-domain image retrieval under the condition of noisy labels (NCIR), a scenario that not only includes the inherent obstacles of traditional cross-domain image retrieval (CIR) but also requires alleviating the adverse effects of label noise. To address this challenge, this paper introduces a novel Robust Domain Alignment framework (RoDA), specifically designed for the NCIR task. At the heart of RoDA is the Selective Division and Adaptive Learning mechanism (SDAL), a key component crafted to shield the model from overfitting the noisy labels. SDAL
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Dissertations / Theses on the topic "Cross-domain retrieval"

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Suyoto, Iman S. H., and ishs@ishs net. "Cross-Domain Content-Based Retrieval of Audio Music through Transcription." RMIT University. Computer Science and Information Technology, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090527.092841.

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Research in the field of music information retrieval (MIR) is concerned with methods to effectively retrieve a piece of music based on a user's query. An important goal in MIR research is the ability to successfully retrieve music stored as recorded audio using note-based queries. In this work, we consider the searching of musical audio using symbolic queries. We first examined the effectiveness of using a relative pitch approach to represent queries and pieces. Our experimental results revealed that this technique, while effective, is optimal when the whole tune is used as a query. We th
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Wigder, Chaya. "Word embeddings for monolingual and cross-language domain-specific information retrieval." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233028.

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Various studies have shown the usefulness of word embedding models for a wide variety of natural language processing tasks. This thesis examines how word embeddings can be incorporated into domain-specific search engines for both monolingual and cross-language search. This is done by testing various embedding model hyperparameters, as well as methods for weighting the relative importance of words to a document or query. In addition, methods for generating domain-specific bilingual embeddings are examined and tested. The system was compared to a baseline that used cosine similarity without word
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Franco, Salvador Marc. "A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/84285.

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Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human languages. One of its most challenging aspects involves enabling computers to derive meaning from human natural language. To do so, several meaning or context representations have been proposed with competitive performance. However, these representations still have room for improvement when working in a cross-domain or cross-language scenario. In this thesis we study the use of knowledge graphs as a cross-domain an
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Dobslaw, Felix. "An Adaptive, Searchable and Extendable Context Model,enabling cross-domain Context Storage, Retrieval and Reasoning : Architecture, Design, Implementation and Discussion." Thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-12179.

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The specification of communication standards and increased availability of sensors for mobile phones and mobile systems are responsible for a significantly increasing sensor availability in populated environments. These devices are able to measure physical parameters and make this data available via communication in sensor networks. To take advantage of the so called acquiring information for public services, other parties have to be able to receive and interpret it. Locally measured datacould be seen as a means of describing user context. For a generic processing of arbitrary context data, a
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Bhowmik, Neelanjan. "Recherche multi-descripteurs dans les fonds photographiques numérisés." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1037/document.

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La recherche d’images par contenu (CBIR) est une discipline de l’informatique qui vise à structurer automatiquement les collections d’images selon des critères visuels. Les fonctionnalités proposées couvrent notamment l’accès efficace aux images dans une grande base de données d’images ou l’identification de leur contenu par des outils de détection et de reconnaissance d’objets. Ils ont un impact sur une large gamme de domaines qui manipulent ce genre de données, telles que le multimedia, la culture, la sécurité, la santé, la recherche scientifique, etc.Indexer une image à partir de son conten
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Fülleborn, Alexander [Verfasser]. "Methods to Create, Retrieve and Apply Cross-Domain Problem Solutions : A Problem-Oriented Pattern Management Approach / Alexander Fülleborn." Aachen : Shaker, 2016. http://d-nb.info/1118259440/34.

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Dutta, Titir. "Generalizing Cross-domain Retrieval Algorithms." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5869.

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Cross-domain retrieval is an important research topic due to its wide range of applications in e-commerce, forensics etc. It addresses the data retrieval problem from a search set, when the query belongs to one domain, and the search database contains samples from some other domain. Several algorithms have been proposed for the same in recent literature to address this task. In this thesis, we address some of the challenges in cross-domain retrieval, specifically for the application of sketch-based image retrieval. Traditionally, cross-domain algorithms assume that both the training and tes
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Lee, Tang, and 李唐. "Cross-Domain Image-Based 3D Shape Retrieval by View Sequence Learning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/sxd2rr.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>105<br>We propose a cross-domain image-based 3D shape retrieval method, which learns a joint embedding space for natural images and 3D shapes in an end-to-end manner. The similarities between images and 3D shapes can be computed as the distances in this embedding space. To better encode a 3D shape, we propose a new feature aggregation method, Cross-View Convolution (CVC), which models a 3D shape as a sequence of rendered views. For bridging the gaps between images and 3D shapes, we propose a Cross-Domain Triplet Neural Network (CDTNN) that incorporates an adaptation
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Books on the topic "Cross-domain retrieval"

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Strous, Leon. Internet of Things. Information Processing in an Increasingly Connected World: First IFIP International Cross-Domain Conference, IFIPIoT 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Poznan, Poland, September 18-19, 2018, Revised Selected Papers. Springer Nature, 2019.

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Jacquemin, Christian, and Didier Bourigault. Term Extraction and Automatic Indexing. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0033.

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Terms are pervasive in scientific and technical documents and their identification is a crucial issue for any application dealing with the analysis, understanding, generation, or translation of such documents. In particular, the ever-growing mass of specialized documentation available on-line, in industrial and governmental archives or in digital libraries, calls for advances in terminology processing for tasks such as information retrieval, cross-language querying, indexing of multimedia documents, translation aids, document routing and summarization, etc. This article presents a new domain o
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Book chapters on the topic "Cross-domain retrieval"

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Liu, Chenlu, Xing Xu, Yang Yang, Huimin Lu, Fumin Shen, and Yanli Ji. "Domain Invariant Subspace Learning for Cross-Modal Retrieval." In MultiMedia Modeling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73600-6_9.

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Sheridan, Páraic, Martin Braschlert, and Peter Schäuble. "Cross-language information retrieval in a Multilingual Legal Domain." In Research and Advanced Technology for Digital Libraries. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0026732.

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Hu, Conghui, and Gim Hee Lee. "Feature Representation Learning for Unsupervised Cross-Domain Image Retrieval." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19836-6_30.

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Kluck, Michael, and Fredric C. Gey. "The Domain-Specific Task of CLEF - Specific Evaluation Strategies in Cross-Language Information Retrieval." In Cross-Language Information Retrieval and Evaluation. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44645-1_5.

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Sacaleanu, Bogdan, and Günter Neumann. "A Cross-Lingual German-English Framework for Open-Domain Question Answering." In Evaluation of Multilingual and Multi-modal Information Retrieval. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74999-8_40.

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Han, Zhichao, Weiwei Chen, ChengJia Huang, Xinpan Yuan, Haoyu Ruan, and Zhihao jiang. "Unsupervised Method for Cross-Modal Retrieval Based on Domain Adaptive Learning." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2268-9_51.

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Zhebel, Vladimir, Denis Zubarev, and Ilya Sochenkov. "Different Approaches in Cross-Language Similar Documents Retrieval in the Legal Domain." In Speech and Computer. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60276-5_65.

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Li, Mingkang, and Yonggang Qi. "XPNet: Cross-Domain Prototypical Network for Zero-Shot Sketch-Based Image Retrieval." In Pattern Recognition and Computer Vision. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18907-4_31.

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Althammer, Sophia, Sebastian Hofstätter, and Allan Hanbury. "Cross-Domain Retrieval in the Legal and Patent Domains: A Reproducibility Study." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72240-1_1.

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Furuya, Takahiko, and Ryutarou Ohbuchi. "Visual Saliency Weighting and Cross-Domain Manifold Ranking for Sketch-Based Image Retrieval." In MultiMedia Modeling. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04114-8_4.

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Conference papers on the topic "Cross-domain retrieval"

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Wang, Sitong, Yan Zhang, Yuqi Xia, Yufeng Wang, and Nian Wang. "DisenCB: Learning Diverse Disentangled Representation for Cross-Domain Consecutive Footprint Retrieval." In 2024 16th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2024. https://doi.org/10.1109/wcsp62071.2024.10827121.

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He, Xuewan, Jielei Wang, Qianxin Xia, Guoming Lu, Yuan Tang, and Hongxia Lu. "Cross-Domain Feature Semantic Calibration for Zero-Shot Sketch-Based Image Retrieval." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687519.

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Song, Mengxiao, Tingwen Liu, Quangang Li, Duohe Ma, Ming Sun, and Ling Tian. "Zero-Shot Cross-Domain Slot Filling with Retrieval Augmented In-Context Learning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889405.

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Liu, Anan, Shu Xiang, Wenhui Li, Weizhi Nie, and Yuting Su. "Cross-Domain 3D Model Retrieval via Visual Domain Adaption." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/115.

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Recent advances in 3D capturing devices and 3D modeling software have led to extensive and diverse 3D datasets, which usually have different distributions. Cross-domain 3D model retrieval is becoming an important but challenging task. However, existing works mainly focus on 3D model retrieval in a closed dataset, which seriously constrain their implementation for real applications. To address this problem, we propose a novel crossdomain 3D model retrieval method by visual domain adaptation. This method can inherit the advantage of deep learning to learn multi-view visual features in the data-d
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Gajic, Bojana, and Ramon Baldrich. "Cross-Domain Fashion Image Retrieval." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2018. http://dx.doi.org/10.1109/cvprw.2018.00243.

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Niu, Hao, Duc Nguyen, Kei Yonekawa, et al. "User-irrelevant Cross-domain Association Analysis for Cross-domain Recommendation with Transfer Learning." In ICMR '23: International Conference on Multimedia Retrieval. ACM, 2023. http://dx.doi.org/10.1145/3592571.3592974.

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Li, Fengxin, Hongyan Liu, and Jun He. "Diffusion Alignment for Cross Domain Recommendation." In ICMR '25: International Conference on Multimedia Retrieval. ACM, 2025. https://doi.org/10.1145/3731715.3733307.

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Wang, Zhipeng, Hao Wang, Jiexi Yan, Aming Wu, and Cheng Deng. "Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/158.

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Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scenario. Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches. Toward this end, we propose a novel Domain-Smoothing Network (DSN) for ZS-SBIR. Specifically, a cross-modal contrastive method is propose
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Ji, Xin, Wei Wang, Meihui Zhang, and Yang Yang. "Cross-Domain Image Retrieval with Attention Modeling." In MM '17: ACM Multimedia Conference. ACM, 2017. http://dx.doi.org/10.1145/3123266.3123429.

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Xu, Bowen, Zhenchang Xing, Xin Xia, David Lo, Qingye Wang, and Shanping Li. "Domain-specific cross-language relevant question retrieval." In ICSE '16: 38th International Conference on Software Engineering. ACM, 2016. http://dx.doi.org/10.1145/2901739.2901746.

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