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'

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Published: 6 September 2023

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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 (May 31, 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 forums to cover all domains, especially those emerging lately with little labeled data but great discrepancy from existing domains. We refer to this scenario as cross-domain question retrieval. To handle the unique challenges of cross-domain question retrieval, we design a model based on adversarial training, namely, X-QR , which consists of two modules—a domain discriminator and a sentence matcher. The domain discriminator aims at aligning the source and target data distributions and unifying the feature space by domain-adversarial training. With the assistance of the domain discriminator, the sentence matcher is able to learn domain-consistent knowledge for the final matching prediction. To the best of our knowledge, this work is among the first to investigate the domain adaption problem of sentence matching for community Q&A forums question retrieval. The experiment results suggest that the proposed X-QR model offers better performance than conventional sentence matching methods in accomplishing cross-domain community Q&A tasks.
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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 (June 26, 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 eliminate the cross-domain gap through In-domain Self-matching Supervision (ISS) and Cross-domain Classifier Alignment (CCA). To be specific, ISS is presented to encapsulate discriminative information into the latent common space by elaborating a novel self-matching supervision mechanism. To alleviate the cross-domain discrepancy, CCA is proposed to align distinct domain-specific classifiers. Thanks to the ISS and CCA, our method could encode the discrimination into the domain-invariant embedding space for unsupervised cross-domain image retrieval. To verify the effectiveness of the proposed method, extensive experiments are conducted on four benchmark datasets compared with six state-of-the-art methods.
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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 (November 4, 2017): 1084–122. http://dx.doi.org/10.1007/s10664-017-9568-3.

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Ikeda, Kanami, Hidenori Suzuki, and Eriko Watanabe. "Optical correlation-based cross-domain image retrieval system." Optics Letters 42, no. 13 (June 29, 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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Zhao, Wentian, Xinxiao Wu, and Jiebo Luo. "Cross-Domain Image Captioning via Cross-Modal Retrieval and Model Adaptation." IEEE Transactions on Image Processing 30 (2021): 1180–92. http://dx.doi.org/10.1109/tip.2020.3042086.

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Pham, Hai X., Ricardo Guerrero, Vladimir Pavlovic, and Jiatong Li. "CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2423–30. http://dx.doi.org/10.1609/aaai.v35i3.16343.

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Despite the abundance of multi-modal data, such as image-text pairs, there has been little effort in understanding the individual entities and their different roles in the construction of these data instances. In this work, we endeavour to discover the entities and their corresponding importance in cooking recipes automatically as a visual-linguistic association problem. More specifically, we introduce a novel cross-modal learning framework to jointly model the latent representations of images and text in the food image-recipe association and retrieval tasks. This model allows one to discover complex functional and hierarchical relationships between images and text, and among textual parts of a recipe including title, ingredients and cooking instructions. Our experiments show that by making use of efficient tree-structured Long Short-Term Memory as the text encoder in our computational cross-modal retrieval framework, we are not only able to identify the main ingredients and cooking actions in the recipe descriptions without explicit supervision, but we can also learn more meaningful feature representations of food recipes, appropriate for challenging cross-modal retrieval and recipe adaption tasks.
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Zi, Lingling, Junping Du, and Qian Wang. "Domain-Oriented Subject Aware Model for Multimedia Data Retrieval." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/429696.

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With the increment of the scale of internet information as well as the cross-correlation interaction, how to achieve accurate retrieval of multimedia data is an urgent question in terms of efficiently utilizing information resources. However, existing information retrieval approaches provide only limited capabilities to search multimedia data. In order to improve the ability of information retrieval, we propose a domain-oriented subject aware model by introducing three innovative improvements. Firstly, we propose the text-image feature mapping method based on the transfer learning to extract image semantics. Then we put forward the annotation document method to accomplish simultaneous retrieval of multimedia data. Lastly, we present subject aware graph to quantify the semantics of query requirements, which can customize query threshold to retrieve multimedia data. Conducted experiments show that our model obtained encouraging performance results.
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Dong, Jianfeng, Zhongzi Long, Xiaofeng Mao, Changting Lin, Yuan He, and Shouling Ji. "Multi-level Alignment Network for Domain Adaptive Cross-modal Retrieval." Neurocomputing 440 (June 2021): 207–19. http://dx.doi.org/10.1016/j.neucom.2021.01.114.

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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 then suggested an algorithm involving the use of pitch classes in conjunction with the longest common subsequence algorithm between a query and target, also using the whole tune as a query. We also proposed an algorithm that works effectively when only a small part of a tune is used as a query. The algorithm makes use of a sliding window in addition to pitch classes and the longest common subsequence algorithm between a query and target. We examined the algorithm using queries based on the beginning, middle, and ending parts of pieces. We performed experiments on an audio collection and manually-constructed symbolic queries. Our experimental evaluation revealed that our techniques are highly effective, with most queries used in our experiments being able to retrieve a correct answer in the first rank position. In addition, we examined the effectiveness of duration-based features for improving retrieval effectiveness over the use of pitch only. We investigated note durations and inter-onset intervals. For this purpose, we used solely symbolic music so that we could focus on the core of the problem. A relative pitch approach alongside a relative duration representation were used in our experiments. Our experimental results showed that durations fail to significantly improve retrieval effectiveness, whereas inter-onset intervals significantly improve retrieval effectiveness.
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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 embeddings, and for both the monolingual and bilingual search engines the use of monolingual embedding models improved performance above the baseline. However, bilingual embeddings, especially for domain-specific terms, tended to be of too poor quality to be used directly in the search engines.
Flera studier har visat att ordinbäddningsmodeller är användningsbara för många olika språkteknologiuppgifter. Denna avhandling undersöker hur ordinbäddningsmodeller kan användas i sökmotorer för både enspråkig och tvärspråklig domänspecifik sökning. Experiment gjordes för att optimera hyperparametrarna till ordinbäddningsmodellerna och för att hitta det bästa sättet att vikta ord efter hur viktiga de är i dokumentet eller sökfrågan. Dessutom undersöktes metoder för att skapa domänspecifika tvåspråkiga inbäddningar. Systemet jämfördes med en baslinje utan inbäddningar baserad på cosinuslikhet, och för både enspråkiga och tvärspråkliga sökningar var systemet som använde enspråkiga inbäddningar bättre än baslinjen. Däremot var de tvåspråkiga inbäddningarna, särskilt för domänspecifika ord, av låg kvalitet och gav för dåliga resultat för direkt användning inom sökmotorer.
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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 and cross-language representation of text and its meaning. A knowledge graph is a graph that expands and relates the original concepts belonging to a set of words. We obtain its characteristics using a wide-coverage multilingual semantic network as knowledge base. This allows to have a language coverage of hundreds of languages and millions human-general and -specific concepts. As starting point of our research we employ knowledge graph-based features - along with other traditional ones and meta-learning - for the NLP task of single- and cross-domain polarity classification. The analysis and conclusions of that work provide evidence that knowledge graphs capture meaning in a domain-independent way. The next part of our research takes advantage of the multilingual semantic network and focuses on cross-language Information Retrieval (IR) tasks. First, we propose a fully knowledge graph-based model of similarity analysis for cross-language plagiarism detection. Next, we improve that model to cover out-of-vocabulary words and verbal tenses and apply it to cross-language document retrieval, categorisation, and plagiarism detection. Finally, we study the use of knowledge graphs for the NLP tasks of community questions answering, native language identification, and language variety identification. The contributions of this thesis manifest the potential of knowledge graphs as a cross-domain and cross-language representation of text and its meaning for NLP and IR tasks. These contributions have been published in several international conferences and journals.
El Procesamiento del Lenguaje Natural (PLN) es un campo de la informática, la inteligencia artificial y la lingüística computacional centrado en las interacciones entre las máquinas y el lenguaje de los humanos. Uno de sus mayores desafíos implica capacitar a las máquinas para inferir el significado del lenguaje natural humano. Con este propósito, diversas representaciones del significado y el contexto han sido propuestas obteniendo un rendimiento competitivo. Sin embargo, estas representaciones todavía tienen un margen de mejora en escenarios transdominios y translingües. En esta tesis estudiamos el uso de grafos de conocimiento como una representación transdominio y translingüe del texto y su significado. Un grafo de conocimiento es un grafo que expande y relaciona los conceptos originales pertenecientes a un conjunto de palabras. Sus propiedades se consiguen gracias al uso como base de conocimiento de una red semántica multilingüe de amplia cobertura. Esto permite tener una cobertura de cientos de lenguajes y millones de conceptos generales y específicos del ser humano. Como punto de partida de nuestra investigación empleamos características basadas en grafos de conocimiento - junto con otras tradicionales y meta-aprendizaje - para la tarea de PLN de clasificación de la polaridad mono- y transdominio. El análisis y conclusiones de ese trabajo muestra evidencias de que los grafos de conocimiento capturan el significado de una forma independiente del dominio. La siguiente parte de nuestra investigación aprovecha la capacidad de la red semántica multilingüe y se centra en tareas de Recuperación de Información (RI). Primero proponemos un modelo de análisis de similitud completamente basado en grafos de conocimiento para detección de plagio translingüe. A continuación, mejoramos ese modelo para cubrir palabras fuera de vocabulario y tiempos verbales, y lo aplicamos a las tareas translingües de recuperación de documentos, clasificación, y detección de plagio. Por último, estudiamos el uso de grafos de conocimiento para las tareas de PLN de respuesta de preguntas en comunidades, identificación del lenguaje nativo, y identificación de la variedad del lenguaje. Las contribuciones de esta tesis ponen de manifiesto el potencial de los grafos de conocimiento como representación transdominio y translingüe del texto y su significado en tareas de PLN y RI. Estas contribuciones han sido publicadas en diversas revistas y conferencias internacionales.
El Processament del Llenguatge Natural (PLN) és un camp de la informàtica, la intel·ligència artificial i la lingüística computacional centrat en les interaccions entre les màquines i el llenguatge dels humans. Un dels seus majors reptes implica capacitar les màquines per inferir el significat del llenguatge natural humà. Amb aquest propòsit, diverses representacions del significat i el context han estat proposades obtenint un rendiment competitiu. No obstant això, aquestes representacions encara tenen un marge de millora en escenaris trans-dominis i trans-llenguatges. En aquesta tesi estudiem l'ús de grafs de coneixement com una representació trans-domini i trans-llenguatge del text i el seu significat. Un graf de coneixement és un graf que expandeix i relaciona els conceptes originals pertanyents a un conjunt de paraules. Les seves propietats s'aconsegueixen gràcies a l'ús com a base de coneixement d'una xarxa semàntica multilingüe d'àmplia cobertura. Això permet tenir una cobertura de centenars de llenguatges i milions de conceptes generals i específics de l'ésser humà. Com a punt de partida de la nostra investigació emprem característiques basades en grafs de coneixement - juntament amb altres tradicionals i meta-aprenentatge - per a la tasca de PLN de classificació de la polaritat mono- i trans-domini. L'anàlisi i conclusions d'aquest treball mostra evidències que els grafs de coneixement capturen el significat d'una forma independent del domini. La següent part de la nostra investigació aprofita la capacitat\hyphenation{ca-pa-ci-tat} de la xarxa semàntica multilingüe i se centra en tasques de recuperació d'informació (RI). Primer proposem un model d'anàlisi de similitud completament basat en grafs de coneixement per a detecció de plagi trans-llenguatge. A continuació, vam millorar aquest model per cobrir paraules fora de vocabulari i temps verbals, i ho apliquem a les tasques trans-llenguatges de recuperació de documents, classificació, i detecció de plagi. Finalment, estudiem l'ús de grafs de coneixement per a les tasques de PLN de resposta de preguntes en comunitats, identificació del llenguatge natiu, i identificació de la varietat del llenguatge. Les contribucions d'aquesta tesi posen de manifest el potencial dels grafs de coneixement com a representació trans-domini i trans-llenguatge del text i el seu significat en tasques de PLN i RI. Aquestes contribucions han estat publicades en diverses revistes i conferències internacionals.
Franco Salvador, M. (2017). A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/84285
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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 model for the specification ofenvironments, users, information sources and information semantics has to be defined. Such a model would, in the optimal case, enable global domain crossing context usage and hence a broader foundation for context interpretation and integration.This thesis proposes the CII-(Context Information Integration) model for the persistence and retrieval of context information in mobile, dynamically changing, environments. It discusses the terms context and context modeling under the analysis of former publications in thefield. Further-more an architecture and prototype are presented.Live and historical data are stored and accessed by the same platform and querying processor, but are treated in an optimized fashion.Optimized retrieval for closeness in n-dimensional context-spaces is supported by a dedicated method. The implementation enables self-aware,shareable agents that are able to reason or act based upon the global context,including their own. These agents can be considered as being a part of the wholecontext, being movable and executable for all context-aware applications.By applying open source technology, a gratifying implementation of CII is feasible. The document contains a thorough discussion concerning the software design and further prototype development. The use cases at the end of the document show the flexibility and extendability of the model and its implementation as a context-base for three entirely different applications.
MediaSense
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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 contenu visuel nécessite d’abord de produire un résumé visuel de ce contenu pour un usage donné, qui sera l’index de cette image dans la collection. En matière de descripteurs d’images, la littérature est désormais trés riche: plusieurs familles de descripteurs existent, et dans chaque famille de nombreuses approches cohabitent. Bon nombre de descripteurs ne décrivant pas la même information et n’ayant pas les mêmes propriétés d’invariance, il peut être pertinent de les combiner de manière à mieux décrire le contenu de l’image. Cette combinaison peut être mise en oeuvre de différentes manières, selon les descripteurs considérés et le but recherché. Dans cette thése, nous nous concentrons sur la famille des descripteurs locaux, avec pour application la recherche d’images ou d’objets par l’exemple dans une collection d’images. Leurs bonnes propriétés les rendent très populaires pour la recherche, la reconnaissance et la catégorisation d'objets et de scènes. Deux directions de recherche sont étudiées:Combinaison de caractéristiques pour la recherche d’images par l’exemple: Le coeur de la thèse repose sur la proposition d’un modèle pour combiner des descripteurs de bas niveau et génériques afin d’obtenir un descripteur plus riche et adapté à un cas d’utilisation donné tout en conservant la généricité afin d’indexer différents types de contenus visuels. L’application considérée étant la recherche par l’exemple, une autre difficulté majeure est la complexité de la proposition, qui doit correspondre à des temps de récupération réduits, même avec de grands ensembles de données. Pour atteindre ces objectifs, nous proposons une approche basée sur la fusion d'index inversés, ce qui permet de mieux représenter le contenu tout en étant associé à une méthode d’accès efficace.Complémentarité des descripteurs: Nous nous concentrons sur l’évaluation de la complémentarité des descripteurs locaux existant en proposant des critères statistiques d’analyse de leur répartition spatiale dans l'image. Ce travail permet de mettre en évidence une synergie entre certaines de ces techniques lorsqu’elles sont jugées suffisamment complémentaires. Les critères spatiaux sont exploités dans un modèle de prédiction à base de régression linéaire, qui a l'avantage de permettre la sélection de combinaisons de descripteurs optimale pour la base considérée mais surtout pour chaque image de cette base. L'approche est évaluée avec le moteur de recherche multi-index, où il montre sa pertinence et met aussi en lumière le fait que la combinaison optimale de descripteurs peut varier d'une image à l'autre.En outre, nous exploitons les deux propositions précédentes pour traiter le problème de la recherche d'images inter-domaines, correspondant notamment à des vues multi-source et multi-date. Deux applications sont explorées dans cette thèse. La recherche d’images inter-domaines est appliquée aux collections photographiques culturelles numérisées d’un musée, où elle démontre son efficacité pour l’exploration et la valorisation de ces contenus à différents niveaux, depuis leur archivage jusqu’à leur exposition ou ex situ. Ensuite, nous explorons l’application de la localisation basée image entre domaines, où la pose d’une image est estimée à partir d’images géoréférencées, en retrouvant des images géolocalisées visuellement similaires à la requête
Content-Based Image Retrieval (CBIR) is a discipline of Computer Science which aims at automatically structuring image collections according to some visual criteria. The offered functionalities include the efficient access to images in a large database of images, or the identification of their content through object detection and recognition tools. They impact a large range of fields which manipulate this kind of data, such as multimedia, culture, security, health, scientific research, etc.To index an image from its visual content first requires producing a visual summary of this content for a given use, which will be the index of this image in the database. From now on, the literature on image descriptors is very rich; several families of descriptors exist and in each family, a lot of approaches live together. Many descriptors do not describe the same information and do not have the same properties. Therefore it is relevant to combine some of them to better describe the image content. The combination can be implemented differently according to the involved descriptors and to the application. In this thesis, we focus on the family of local descriptors, with application to image and object retrieval by example in a collection of images. Their nice properties make them very popular for retrieval, recognition and categorization of objects and scenes. Two directions of research are investigated:Feature combination applied to query-by-example image retrieval: the core of the thesis rests on the proposal of a model for combining low-level and generic descriptors in order to obtain a descriptor richer and adapted to a given use case while maintaining genericity in order to be able to index different types of visual contents. The considered application being query-by-example, another major difficulty is the complexity of the proposal, which has to meet with reduced retrieval times, even with large datasets. To meet these goals, we propose an approach based on the fusion of inverted indices, which allows to represent the content better while being associated with an efficient access method.Complementarity of the descriptors: We focus on the evaluation of the complementarity of existing local descriptors by proposing statistical criteria of analysis of their spatial distribution. This work allows highlighting a synergy between some of these techniques when judged sufficiently complementary. The spatial criteria are employed within a regression-based prediction model which has the advantage of selecting the suitable feature combinations globally for a dataset but most importantly for each image. The approach is evaluated within the fusion of inverted indices search engine, where it shows its relevance and also highlights that the optimal combination of features may vary from an image to another.Additionally, we exploit the previous two proposals to address the problem of cross-domain image retrieval, where the images are matched across different domains, including multi-source and multi-date contents. Two applications of cross-domain matching are explored. First, cross-domain image retrieval is applied to the digitized cultural photographic collections of a museum, where it demonstrates its effectiveness for the exploration and promotion of these contents at different levels from their archiving up to their exhibition in or ex-situ. Second, we explore the application of cross-domain image localization, where the pose of a landmark is estimated by retrieving visually similar geo-referenced images to the query images
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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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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
國立臺灣大學
電機工程學研究所
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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 layer to match the features from different domains better and can be trained end-to-end. In addition, we speed up the triplet training process by presenting a new fast cross-domain triplet neural network architecture. We evaluate our method on a new image to 3D shape dataset. Experimental results demonstrate that our method outperforms the state-of-the-art approaches in terms of retrieval performance. We also provide in-depth analysis of various design choices to further reduce the memory storage and computational cost.
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Books on the topic "Cross-domain retrieval"

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Josef, Basl, You Ilsun, Xu Lida, Weippl Edgar, and SpringerLink (Online service), eds. Multidisciplinary Research and Practice for Information Systems: IFIP WG 8.4, 8.9/TC 5 International Cross-Domain Conference and Workshop on Availability, Reliability, and Security, CD-ARES 2012, Prague, Czech Republic, August 20-24, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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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. Cham: 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 of research and development in natural language processing (NLP) that is concerned with the representation, acquisition, and recognition of terms. It begins with presenting the basic notions about the concept of ‘terms’, ranging from the classical view, to the recent concepts. There are two main areas of research involving terminology in NLP, which are, term acquisition and term recognition. Finally, this article presents the recent advances and prospects in term acquisition and automatic indexing.
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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, 94–105. Cham: 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, 253–68. Berlin, Heidelberg: 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, 529–44. Cham: 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, 48–56. Berlin, Heidelberg: 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, 328–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74999-8_40.

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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, 679–86. Cham: 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, 394–410. Cham: 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, 3–17. Cham: 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, 37–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04114-8_4.

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Gao, Kai, Jian Zhang, Chen Li, Changbo Wang, Gaoqi He, and Hong Qin. "Novel Sketch-Based 3D Model Retrieval via Cross-domain Feature Clustering and Matching." In Artificial Neural Networks and Machine Learning – ICANN 2020, 299–311. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61609-0_24.

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Conference papers on the topic "Cross-domain retrieval"

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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}. California: 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-driven manner for 3D model representation. Moreover, it can reduce the domain divergence by exploiting both domainshared and domain-specific features of different domains. Consequently, it can augment the discrimination of visual descriptors for cross-domain similarity measure. Extensive experiments on two popular datasets, under three designed cross-domain scenarios, demonstrate the superiority and effectiveness of the proposed method by comparing against the state-of-the-art methods. Especially, the proposed method can significantly outperform the most recent method for cross-domain 3D model retrieval and the champion of Shrec’16 Large-Scale 3D Shape Retrieval from ShapeNet Core55.
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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, Mori Kurokawa, Chihiro Ono, Daichi Amagata, Takuya Maekawa, and Takahiro Hara. "User-irrelevant Cross-domain Association Analysis for Cross-domain Recommendation with Transfer Learning." In ICMR '23: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3592571.3592974.

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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. New York, NY, USA: 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. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2901739.2901746.

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Liu, Chenlu, Huimin Lu, Hao Wei, Xing Xu, and Yanli Ji. "Domain separation network for cross-modal retrieval." In ICIMCS'18: The 10th International Conference on Internet Multimedia Computing and Service. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240876.3240878.

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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}. California: 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 proposed to learn generalized representations to smooth the domain gap by mining relations with additional augmented samples. Furthermore, a category-specific memory bank with sketch features is explored to reduce intra-class diversity in the sketch domain. Extensive experiments demonstrate that our approach notably outperforms the state-of-the-art methods in both Sketchy and TU-Berlin datasets.
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Liu, Yang, Qingchao Chen, and Samuel Albanie. "Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.01471.

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Huang, Xin, Yuxin Peng, and Mingkuan Yuan. "Cross-modal Common Representation Learning by Hybrid Transfer Network." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/263.

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DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross-modal training data. In single-modal scenario, similar problem is usually relieved by transferring knowledge from large-scale auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is also very useful for cross-modal retrieval, which can provide rich general semantic information that can be shared across different modalities. However, it is challenging to transfer useful knowledge from single-modal (as image) source domain to cross-modal (as image/text) target domain. Knowledge in source domain cannot be directly transferred to both two different modalities in target domain, and the inherent cross-modal correlation contained in target domain provides key hints for cross-modal retrieval which should be preserved during transfer process. This paper proposes Cross-modal Hybrid Transfer Network (CHTN) with two subnetworks: Modal-sharing transfer subnetwork utilizes the modality in both source and target domains as a bridge, for transferring knowledge to both two modalities simultaneously; Layer-sharing correlation subnetwork preserves the inherent cross-modal semantic correlation to further adapt to cross-modal retrieval task. Cross-modal data can be converted to common representation by CHTN for retrieval, and comprehensive experiment on 3 datasets shows its effectiveness.
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Rafailidis, Dimitrios, and Fabio Crestani. "Neural Attentive Cross-Domain Recommendation." In ICTIR '19: The 2019 ACM SIGIR International Conference on the Theory of Information Retrieval. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341981.3344214.

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