Journal articles: '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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Peng, Yuxin, and Jingze Chi. "Unsupervised Cross-Media Retrieval Using Domain Adaptation With Scene Graph." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 11 (November 2020): 4368–79. http://dx.doi.org/10.1109/tcsvt.2019.2953692.

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Zhuo, Zheng, and Zhong Zhou. "Cross-domain remote sensing image retrieval with Gabor-based CNN." International Journal of Remote Sensing 44, no. 2 (January 17, 2023): 567–84. http://dx.doi.org/10.1080/01431161.2023.2168136.

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Park, Eunhwan, Sung-Min Lee, Dearyong Seo, Seonhoon Kim, Inho Kang, and Seung-Hoon Na. "RINK: Reader-Inherited Evidence Reranker for Table-and-Text Open Domain Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 13446–56. http://dx.doi.org/10.1609/aaai.v37i11.26577.

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Most approaches used in open-domain question answering on hybrid data that comprises both tabular-and-textual contents are based on a Retrieval-Reader pipeline in which the retrieval module finds relevant “heterogenous” evidence for a given question and the reader module generates an answer from the retrieved evidence. In this paper, we present a Retriever-Reranker-Reader framework by newly proposing a Reader-INherited evidence reranKer (RINK) where a reranker module is designed by finetuning the reader’s neural architecture based on a simple prompting method. Our underlying assumption of reusing the reader’s module for the reranker is that the reader’s ability to generating an answer from evidence contains the knowledge required for the reranking, because the reranker needs to “read” in-depth a question and evidences more carefully and elaborately than a baseline retriever. Furthermore, we present a simple and effective pretraining method by extensively deploying the commonly used data augmentation methods of cell corruption and cell reordering based on the pretraining tasks - tabular-and-textual entailment and cross-modal masked language modeling. Experimental results on OTT-QA, a large-scale table-and-text open-domain question answering dataset, show that the proposed RINK armed with our pretraining procedure makes improvements over the baseline reranking method and leads to state-of-the-art performance.

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Zhang, Shu Dong, Can Zhang, and Jing Wang. "Research of Intelligent Search Engine Based on Multi-Ontology." Applied Mechanics and Materials 241-244 (December 2012): 1659–63. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1659.

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With the development of the Semantic Web, ontology has become the primary means of expression of many fields of knowledge. Introducing the Semantic Web technology into the field of search engine is a valuable research topic. In order to meet the complex semantic retrieval demands, the paper proposes a search engine model based on multi-domain ontology, the model using ontology mapping rewrite the user query to achieve multiple ontology query, and provide a richer and accurate semantic information for the retrieval of cross-domain knowledge; And the paper proposes a method of cross-domain ontology annotation, providing a basis for the user semantic retrieval. The experimental results show that the search results improve the precision and recall rate.

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Lei, Haopeng, Simin Chen, Mingwen Wang, Xiangjian He, Wenjing Jia, and Sibo Li. "A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation." Wireless Communications and Mobile Computing 2021 (May 25, 2021): 1–14. http://dx.doi.org/10.1155/2021/5577735.

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Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.

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Liu, An-An, Shu Xiang, Wei-Zhi Nie, and Dan Song. "End-to-End Visual Domain Adaptation Network for Cross-Domain 3D CPS Data Retrieval." IEEE Access 7 (2019): 118630–38. http://dx.doi.org/10.1109/access.2019.2937377.

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Chen, Qingchao, Yang Liu, and Samuel Albanie. "Mind-the-Gap! Unsupervised Domain Adaptation for Text-Video Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1072–80. http://dx.doi.org/10.1609/aaai.v35i2.16192.

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When can we expect a text-video retrieval system to work effectively on datasets that differ from its training domain? In this work, we investigate this question through the lens of unsupervised domain adaptation in which the objective is to match natural language queries and video content in the presence of domain shift at query-time. Such systems have significant practical applications since they are capable generalising to new data sources without requiring corresponding text annotations. We make the following contributions: (1) We propose the UDAVR (Unsupervised Domain Adaptation for Video Retrieval) benchmark and employ it to study the performance of text-video retrieval in the presence of domain shift. (2) We propose Concept-Aware-Pseudo-Query (CAPQ), a method for learning discriminative and transferable features that bridge these cross-domain discrepancies to enable effective target domain retrieval using source domain supervision. (3) We show that CAPQ outperforms alternative domain adaptation strategies on UDAVR.

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Ahmad Khan, Aatif, and Sanjay Kumar Malik. "Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search." Mehran University Research Journal of Engineering and Technology 39, no. 3 (July 1, 2020): 595–602. http://dx.doi.org/10.22581/muet1982.2003.14.

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Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.

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Miao, Yongwei, Gaoyi Li, Chen Bao, Jiajing Zhang, and Jinrong Wang. "ClothingNet: Cross-Domain Clothing Retrieval With Feature Fusion and Quadruplet Loss." IEEE Access 8 (2020): 142669–79. http://dx.doi.org/10.1109/access.2020.3013631.

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Bai, Cong, Jian Chen, Qing Ma, Pengyi Hao, and Shengyong Chen. "Cross-domain representation learning by domain-migration generative adversarial network for sketch based image retrieval." Journal of Visual Communication and Image Representation 71 (August 2020): 102835. http://dx.doi.org/10.1016/j.jvcir.2020.102835.

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Li, Wen-Hui, Shu Xiang, Wei-Zhi Nie, Dan Song, An-An Liu, Xuan-Ya Li, and Tong Hao. "Joint deep feature learning and unsupervised visual domain adaptation for cross-domain 3D object retrieval." Information Processing & Management 57, no. 5 (September 2020): 102275. http://dx.doi.org/10.1016/j.ipm.2020.102275.

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Yu Lingzhi, 于凌志, and 张熙凡 Zhang Xifan. "基于跨域联合空间注意网络的草图图像检索." Laser & Optoelectronics Progress 59, no. 22 (2022): 2215009. http://dx.doi.org/10.3788/lop202259.2215009.

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Alirezazadeh, Pendar, Fadi Dornaika, and Abdelmalik Moujahid. "Deep Learning with Discriminative Margin Loss for Cross-Domain Consumer-to-Shop Clothes Retrieval." Sensors 22, no. 7 (March 30, 2022): 2660. http://dx.doi.org/10.3390/s22072660.

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Consumer-to-shop clothes retrieval refers to the problem of matching photos taken by customers with their counterparts in the shop. Due to some problems, such as a large number of clothing categories, different appearances of clothing items due to different camera angles and shooting conditions, different background environments, and different body postures, the retrieval accuracy of traditional consumer-to-shop models is always low. With advances in convolutional neural networks (CNNs), the accuracy of garment retrieval has been significantly improved. Most approaches addressing this problem use single CNNs in conjunction with a softmax loss function to extract discriminative features. In the fashion domain, negative pairs can have small or large visual differences that make it difficult to minimize intraclass variance and maximize interclass variance with softmax. Margin-based softmax losses such as Additive Margin-Softmax (aka CosFace) improve the discriminative power of the original softmax loss, but since they consider the same margin for the positive and negative pairs, they are not suitable for cross-domain fashion search. In this work, we introduce the cross-domain discriminative margin loss (DML) to deal with the large variability of negative pairs in fashion. DML learns two different margins for positive and negative pairs such that the negative margin is larger than the positive margin, which provides stronger intraclass reduction for negative pairs. The experiments conducted on publicly available fashion datasets DARN and two benchmarks of the DeepFashion dataset—(1) Consumer-to-Shop Clothes Retrieval and (2) InShop Clothes Retrieval—confirm that the proposed loss function not only outperforms the existing loss functions but also achieves the best performance.

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CHIU, IVEY, and L. H. SHU. "Biomimetic design through natural language analysis to facilitate cross-domain information retrieval." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 21, no. 1 (January 2007): 45–59. http://dx.doi.org/10.1017/s0890060407070138.

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Biomimetic, or biologically inspired, design uses analogous biological phenomena to develop solutions for engineering problems. Several instances of biomimetic design result from personal observations of biological phenomena. However, many engineers' knowledge of biology may be limited, thus reducing the potential of biologically inspired solutions. Our approach to biomimetic design takes advantage of the large amount of biological knowledge already available in books, journals, and so forth, by performing keyword searches on these existing natural-language sources. Because of the ambiguity and imprecision of natural language, challenges inherent to natural language processing were encountered. One challenge of retrieving relevant cross-domain information involves differences in domain vocabularies, or lexicons. A keyword meaningful to biologists may not occur to engineers. For an example problem that involved cleaning, that is, removing dirt, a biochemist suggested the keyword “defend.” Defend is not an obvious keyword to most engineers for this problem, nor are the words defend and “clean/remove” directly related within lexical references. However, previous work showed that biological phenomena retrieved by the keyword defend provided useful stimuli and produced successful concepts for the clean/remove problem. In this paper, we describe a method to systematically bridge the disparate biology and engineering domains using natural language analysis. For the clean/remove example, we were able to algorithmically generate several biologically meaningful keywords, including defend, that are not obviously related to the engineering problem. We developed a method to organize and rank the set of biologically meaningful keywords identified, and confirmed that we could achieve similar results for two other examples in encapsulation and microassembly. Although we specifically address cross-domain information retrieval from biology, the bridging process presented in this paper is not limited to biology, and can be used for any other domain given the availability of appropriate domain-specific knowledge sources and references.

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Zhan, Huijing, Boxin Shi, and Alex C. Kot. "Cross-Domain Shoe Retrieval With a Semantic Hierarchy of Attribute Classification Network." IEEE Transactions on Image Processing 26, no. 12 (December 2017): 5867–81. http://dx.doi.org/10.1109/tip.2017.2736346.

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Schulz, S., U. Hahn, and K. Markó. "MorphoSaurus." Methods of Information in Medicine 44, no. 04 (2005): 537–45. http://dx.doi.org/10.1055/s-0038-1634005.

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Summary Objectives: We propose an interlingua-based indexing approach to account for the particular challenges that arise in the design and implementation of cross-language document retrieval systems for the medical domain. Methods: Documents, as well as queries, are mapped to a language-independent conceptual layer on which retrieval operations are performed. We contrast this approach with the direct translation of German queries to English ones which, subsequently, are matched against English documents. Results: We evaluate both approaches, interlingua-based and direct translation, on a large medical document collection, the OHSUMED corpus. A substantial benefit for interlingua-based document retrieval using German queries on English texts is found, which amounts to 93% of the (monolingual) English baseline. Conclusions: Most state-of-the-art cross-language information retrieval systems translate user queries to the language(s) of the target documents. In contradistinction to this approach, translating both documents and user queries into a language-independent, concept-like representation format is more beneficial to enhance cross-language retrieval performance.

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Yang, Jingcheng, Qianqian Wang, Tiwei Tao, Sijie Niu, and Mingxia Liu. "Deep Hash with Optimal Transport-Based Domain Adaptation for Multisite MRI Retrieval." Wireless Communications and Mobile Computing 2022 (September 14, 2022): 1–14. http://dx.doi.org/10.1155/2022/8797604.

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The Internet of Things has a wide range of applications in the medical field. Due to the heterogeneity of medical data generated by different hospitals, it is very important to analyze and integrate data from different institutions. Functional magnetic resonance imaging (fMRI) is widely used in clinical medicine and cognitive neuroscience, while resting-state fMRI (rs-fMRI) can help reveal functional biomarkers of neurological disorders for computer-assisted clinical diagnosis and prognosis. Recently, how to retrieve similar images or case histories from large-scale medical image repositories acquired from multiple sites has attracted widespread attention in the field of intelligent diagnosis of diseases. Although using multisite data effectively helps increase the sample size, it also inevitably introduces the problem of data heterogeneity across sites. To address this problem, we propose a multisite fMRI retrieval (MSFR) method that uses a deep hashing approach and an optimal transport-based domain adaptation strategy to mitigate multisite data heterogeneity for accurate fMRI search. Specifically, for a given target domain site and multiple source domain sites, our approach uses a deep neural network to map the source and target domain data into the latent feature space and minimize their Wasserstein distance to reduce their distribution differences. We then use the source domain data to learn high-quality hash code through a global similarity metric, thereby improving the performance of cross-site fMRI retrieval. We evaluated our method on the publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset. Experimental results show the effectiveness of our method in resting-state fMRI retrieval.

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Thanh Nguyen, Tung, Tho Thanh Quan, and Tuoi Thi Phan. "Sentiment search: an emerging trend on social media monitoring systems." Aslib Journal of Information Management 66, no. 5 (September 9, 2014): 553–80. http://dx.doi.org/10.1108/ajim-12-2013-0141.

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Purpose – The purpose of this paper is to discuss sentiment search, which not only retrieves data related to submitted keywords but also identifies sentiment opinion implied in the retrieved data and the subject targeted by this opinion. Design/methodology/approach – The authors propose a retrieval framework known as Cross-Domain Sentiment Search (CSS), which combines the usage of domain ontologies with specific linguistic rules to handle sentiment terms in textual data. The CSS framework also supports incrementally enriching domain ontologies when applied in new domains. Findings – The authors found that domain ontologies are extremely helpful when CSS is applied in specific domains. In the meantime, the embedded linguistic rules make CSS achieve better performance as compared to data mining techniques. Research limitations/implications – The approach has been initially applied in a real social monitoring system of a professional IT company. Thus, it is proved to be able to handle real data acquired from social media channels such as electronic newspapers or social networks. Originality/value – The authors have placed aspect-based sentiment analysis in the context of semantic search and introduced the CSS framework for the whole sentiment search process. The formal definitions of Sentiment Ontology and aspect-based sentiment analysis are also presented. This distinguishes the work from other related works.

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Song, Yuxin, Jianjun Lei, Bo Peng, Kaifu Zheng, Bolan Yang, and Yalong Jia. "Edge-Guided Cross-Domain Learning with Shape Regression for Sketch-Based Image Retrieval." IEEE Access 7 (2019): 32393–99. http://dx.doi.org/10.1109/access.2019.2903534.

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Pham, Quang-Hieu, Mikaela Angelina Uy, Binh-Son Hua, Duc Thanh Nguyen, Gemma Roig, and Sai-Kit Yeung. "LCD: Learned Cross-Domain Descriptors for 2D-3D Matching." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11856–64. http://dx.doi.org/10.1609/aaai.v34i07.6859.

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In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network that maps 2D and 3D input into a shared latent space representation. We show that such local cross-domain descriptors in the shared embedding are more discriminative than those obtained from individual training in 2D and 3D domains. To facilitate the training process, we built a new dataset by collecting ≈ 1.4 millions of 2D-3D correspondences with various lighting conditions and settings from publicly available RGB-D scenes. Our descriptor is evaluated in three main experiments: 2D-3D matching, cross-domain retrieval, and sparse-to-dense depth estimation. Experimental results confirm the robustness of our approach as well as its competitive performance not only in solving cross-domain tasks but also in being able to generalize to solve sole 2D and 3D tasks. Our dataset and code are released publicly at https://hkust-vgd.github.io/lcd.

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Liu, Kang, Jian Yang, and Shengyang Li. "Remote-Sensing Cross-Domain Scene Classification: A Dataset and Benchmark." Remote Sensing 14, no. 18 (September 16, 2022): 4635. http://dx.doi.org/10.3390/rs14184635.

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Domain adaptation for classification has achieved significant progress in natural images but not in remote-sensing images due to huge differences in data-imaging mechanisms between different modalities and inconsistencies in class labels among existing datasets. More importantly, the lack of cross-domain benchmark datasets has become a major obstacle to the development of scene classification in multimodal remote-sensing images. In this paper, we present a cross-domain dataset of multimodal remote-sensing scene classification (MRSSC). The proposed MRSSC dataset contains 26,710 images of 7 typical scene categories with 4 distinct domains that are collected from Tiangong-2, a Chinese manned spacecraft. Based on this dataset, we evaluate several representative domain adaptation algorithms on three cross-domain tasks to build baselines for future research. The results demonstrate that the domain adaptation algorithm can reduce the differences in data distribution between different domains and improve the accuracy of the three tasks to varying degrees. Furthermore, MRSSC also achieved fairly results in three applications: cross-domain data annotation, weakly supervised object detection and data retrieval. This dataset is believed to stimulate innovative research ideas and methods in remote-sensing cross-domain scene classification and remote-sensing intelligent interpretation.

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Venkatesh, V., and S. J. Frasier. "Simulation of Spaced Antenna Wind Retrieval Performance on an X-Band Active Phased Array Weather Radar." Journal of Atmospheric and Oceanic Technology 30, no. 7 (July 1, 2013): 1447–59. http://dx.doi.org/10.1175/jtech-d-11-00203.1.

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Abstract Spaced antenna baseline wind retrievals, in conjunction with traditional Doppler measurements, are a potential means of fine angular resolution weather radar wind vector retrieval. A spaced antenna implementation on an X-band active phased array architecture is investigated via Monte Carlo simulations of the backscattered electric fields at the antenna array. Several retrieval methods are exercised on the data produced by the simulator. Parameters of the X-band spaced-antenna design are then optimized. Benefiting from the parametric fitting procedure inherent in the time domain slope at zero lag and full correlation analysis, the study finds both of these algorithms to be more immune to thermal noise than the spectral retrieval algorithms investigated. With appropriately chosen baselines, these time domain algorithms are shown to perform adequately for 5-dB SNR and above. The study also shows that the Gaussian slope at zero lag (G-SZL) algorithm leads to more robust estimates over a wider range of beamwidths than the Gaussian full correlation analysis (G-FCA) algorithm. The predicted performance of the X-band array is compared to a similar spaced antenna implementation on the S-band National Weather Radar Testbed (NWRT). Since the X-band signal decorrelates more rapidly (relative to S band), the X-band array accumulates more independent samples, thereby obtaining lower retrieval uncertainty. However, the same rapid decorrelation also limits the maximum range of the X-band array, as the pulse rate must be sufficiently high to sample the cross-correlation function. It also limits the range of tolerable turbulence velocity within the resolution cell.

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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 (April 17, 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 cross-domain communication and resource-sharing. Traditional centralized access control schemes are prone to single-point failure problems. Recently, many researchers have integrated blockchain technology into access control models. However, most blockchain-based approaches use a single-chain structure, which has weak data management capability and scalability, while ensuring system security, and low access control efficiency, making it difficult to meet the needs of multi-domain cooperation in IIoT scenarios. Therefore, this paper proposes a decentralized cross-domain access model based on a master–slave chain with high scalability. Moreover, the model ensures the security and reliability of the master chain through a reputation-based node selection mechanism. Access control efficiency is improved by a grouping strategy retrieval method in the access control process. The experimental benchmarks of the proposed scheme use various performance metrics to highlight its applicability in the IIoT environment. The results show an 82% improvement in the throughput for the master–slave chain structure over the single-chain structure. There is also an improvement in the throughput and latency compared to the results of other studies.

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Nothwehr, Steven F., Seon-Ah Ha, and Paul Bruinsma. "Sorting of Yeast Membrane Proteins into an Endosome-to-Golgi Pathway Involves Direct Interaction of Their Cytosolic Domains with Vps35p." Journal of Cell Biology 151, no. 2 (October 16, 2000): 297–310. http://dx.doi.org/10.1083/jcb.151.2.297.

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Resident late-Golgi membrane proteins in Saccharomyces cerevisiae are selectively retrieved from a prevacuolar–endosomal compartment, a process dependent on aromatic amino acid–based sorting determinants on their cytosolic domains. The formation of retrograde vesicles from the prevacuolar compartment and the selective recruitment of vesicular cargo are thought to be mediated by a peripheral membrane retromer protein complex. We previously described mutations in one of the retromer subunit proteins, Vps35p, which caused cargo-specific defects in retrieval. By genetic and biochemical means we now show that Vps35p directly associates with the cytosolic domains of cargo proteins. Chemical cross-linking, followed by coimmunoprecipitation, demonstrated that Vps35p interacts with the cytosolic domain of A-ALP, a model late-Golgi membrane protein, in a retrieval signal–dependent manner. Furthermore, mutations in the cytosolic domains of A-ALP and another cargo protein, Vps10p, were identified that suppressed cargo-specific mutations in Vps35p but did not suppress the retrieval defects of a vps35 null mutation. Suppression was shown to be due to an improvement in protein sorting at the prevacuolar compartment. These data strongly support a model in which Vps35p acts as a “receptor” protein for recognition of the retrieval signal domains of cargo proteins during their recruitment into retrograde vesicles.

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Zhang, Jin, Yuehua Zhao, Xin Cai, Taowen Le, Wei Fei, and Feicheng Ma. "A Comparison of Retrieval Result Relevance Judgments Between American and Chinese Users." Journal of Global Information Management 28, no. 3 (July 2020): 148–68. http://dx.doi.org/10.4018/jgim.2020070108.

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Relevance judgment plays an extremely significant role in information retrieval. This study investigates the differences between American users and Chinese users in relevance judgment during the information retrieval process. 384 sets of relevance scores with 50 scores in each set were collected from 16 American users and 16 Chinese users as they judged retrieval records from two major search engines based on 24 predefined search tasks from 4 domain categories. Statistical analyses reveal that there are significant differences between American assessors and Chinese assessors in relevance judgments. Significant gender differences also appear within both the American and the Chinese assessor groups. The study also revealed significant interactions among cultures, genders, and subject categories. These findings can enhance the understanding of cultural impact on information retrieval and can assist in the design of effective cross-language information retrieval systems.

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Sheng, Shurong, Katrien Laenen, Luc Van Gool, and Marie-Francine Moens. "Fine-Grained Cross-Modal Retrieval for Cultural Items with Focal Attention and Hierarchical Encodings." Computers 10, no. 9 (August 25, 2021): 105. http://dx.doi.org/10.3390/computers10090105.

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In this paper, we target the tasks of fine-grained image–text alignment and cross-modal retrieval in the cultural heritage domain as follows: (1) given an image fragment of an artwork, we retrieve the noun phrases that describe it; (2) given a noun phrase artifact attribute, we retrieve the corresponding image fragment it specifies. To this end, we propose a weakly supervised alignment model where the correspondence between the input training visual and textual fragments is not known but their corresponding units that refer to the same artwork are treated as a positive pair. The model exploits the latent alignment between fragments across modalities using attention mechanisms by first projecting them into a shared common semantic space; the model is then trained by increasing the image–text similarity of the positive pair in the common space. During this process, we encode the inputs of our model with hierarchical encodings and remove irrelevant fragments with different indicator functions. We also study techniques to augment the limited training data with synthetic relevant textual fragments and transformed image fragments. The model is later fine-tuned by a limited set of small-scale image–text fragment pairs. We rank the test image fragments and noun phrases by their intermodal similarity in the learned common space. Extensive experiments demonstrate that our proposed models outperform two state-of-the-art methods adapted to fine-grained cross-modal retrieval of cultural items for two benchmark datasets.

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Costa Pereira, Jose, and Nuno Vasconcelos. "Cross-modal domain adaptation for text-based regularization of image semantics in image retrieval systems." Computer Vision and Image Understanding 124 (July 2014): 123–35. http://dx.doi.org/10.1016/j.cviu.2014.03.003.

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Kiesewetter, Jan, Frank Fischer, and Martin R. Fischer. "Collaboration Expertise in Medicine - No Evidence for Cross-Domain Application from a Memory Retrieval Study." PLOS ONE 11, no. 2 (February 11, 2016): e0148754. http://dx.doi.org/10.1371/journal.pone.0148754.

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Jiao, Shichao, Xie Han, Fengguang Xiong, Fusheng Sun, Rong Zhao, and Liqun Kuang. "Cross-Domain Correspondence for Sketch-Based 3D Model Retrieval Using Convolutional Neural Network and Manifold Ranking." IEEE Access 8 (2020): 121584–95. http://dx.doi.org/10.1109/access.2020.3006585.

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HAMRICK, PHILLIP, and MICHAEL T. ULLMAN. "A neurocognitive perspective on retrieval interference in L2 sentence processing." Bilingualism: Language and Cognition 20, no. 4 (August 1, 2016): 687–88. http://dx.doi.org/10.1017/s136672891600081x.

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Cunnings (Cunnings) offers an interpretation of L2-L1 sentence processing differences in terms of memory principles. We applaud such cross-domain approaches, which seem likely to significantly elucidate the neurocognition of language. Cunnings attributes sentence processing differences between (adult) high proficiency L2 and L1 speakers to an increased susceptibility to similarity-based retrieval interference, rather than to qualitative L2-L1 processing differences (cf. Clahsen & Felser, 2006). On his account, both L1 and L2 sentence processing depend upon a ‘bipartite’ working memory, which involves maintaining items active by focusing attention on long-term memory representations (Cowan, 2001).

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Xiang, Jun, Ruru Pan, and Weidong Gao. "An Efficient Retrieval System Framework for Fabrics Based on Fine-Grained Similarity." Entropy 24, no. 9 (September 19, 2022): 1319. http://dx.doi.org/10.3390/e24091319.

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In the context of “double carbon”, as a traditional high energy consumption industry, the textile industry is facing the severe challenges of energy saving and emission reduction. To improve production efficiency in the textile industry, we propose the use of content-based image retrieval technology to shorten the fabric production cycle. However, fabric retrieval has high requirements for results, which makes it difficult for common retrieval methods to be directly applied to fabric retrieval. This paper presents a novel method for fabric image retrieval. Firstly, we define a fine-grained similarity to measure the similarity between two fabric images. Then, a convolutional neural network with a compact structure and cross-domain connections is designed to narrow the gap between fabric images and similarities. To overcome the problems of probabilistic missing and difficult training in classical hashing, we introduce a variational network module and structural module into the hashing model, which is called DVSH. We employ list-wise learning to perform similarity embedding. The experimental results demonstrate the superiority and efficiency of the proposed hashing model, DVSH.

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Han, Ning, Jingjing Chen, Hao Zhang, Huanwen Wang, and Hao Chen. "Adversarial Multi-Grained Embedding Network for Cross-Modal Text-Video Retrieval." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 2 (May 31, 2022): 1–23. http://dx.doi.org/10.1145/3483381.

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Cross-modal retrieval between texts and videos has received consistent research interest in the multimedia community. Existing studies follow a trend of learning a joint embedding space to measure the distance between text and video representations. In common practice, video representation is constructed by feeding clips into 3D convolutional neural networks for a coarse-grained global visual feature extraction. In addition, several studies have attempted to align the local objects of video with the text. However, these representations share a drawback of neglecting rich fine-grained relation features capturing spatial-temporal object interactions that benefits mapping textual entities in the real-world retrieval system. To tackle this problem, we propose an adversarial multi-grained embedding network (AME-Net), a novel cross-modal retrieval framework that adopts both fine-grained local relation and coarse-grained global features in bridging text-video modalities. Additionally, with the newly proposed visual representation, we also integrate an adversarial learning strategy into AME-Net, to further narrow the domain gap between text and video representations. In summary, we contribute AME-Net with an adversarial learning strategy for learning a better joint embedding space, and experimental results on MSR-VTT and YouCook2 datasets demonstrate that our proposed framework consistently outperforms the state-of-the-art method.

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Liu, Weiquan, Cheng Wang, Xuesheng Bian, Shuting Chen, Wei Li, Xiuhong Lin, Yongchuan Li, Dongdong Weng, Shang-Hong Lai, and Jonathan Li. "AE-GAN-Net: Learning Invariant Feature Descriptor to Match Ground Camera Images and a Large-Scale 3D Image-Based Point Cloud for Outdoor Augmented Reality." Remote Sensing 11, no. 19 (September 26, 2019): 2243. http://dx.doi.org/10.3390/rs11192243.

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Establishing the spatial relationship between 2D images captured by real cameras and 3D models of the environment (2D and 3D space) is one way to achieve the virtual–real registration for Augmented Reality (AR) in outdoor environments. In this paper, we propose to match the 2D images captured by real cameras and the rendered images from the 3D image-based point cloud to indirectly establish the spatial relationship between 2D and 3D space. We call these two kinds of images as cross-domain images, because their imaging mechanisms and nature are quite different. However, unlike real camera images, the rendered images from the 3D image-based point cloud are inevitably contaminated with image distortion, blurred resolution, and obstructions, which makes image matching with the handcrafted descriptors or existing feature learning neural networks very challenging. Thus, we first propose a novel end-to-end network, AE-GAN-Net, consisting of two AutoEncoders (AEs) with Generative Adversarial Network (GAN) embedding, to learn invariant feature descriptors for cross-domain image matching. Second, a domain-consistent loss function, which balances image content and consistency of feature descriptors for cross-domain image pairs, is introduced to optimize AE-GAN-Net. AE-GAN-Net effectively captures domain-specific information, which is embedded into the learned feature descriptors, thus making the learned feature descriptors robust against image distortion, variations in viewpoints, spatial resolutions, rotation, and scaling. Experimental results show that AE-GAN-Net achieves state-of-the-art performance for image patch retrieval with the cross-domain image patch dataset, which is built from real camera images and the rendered images from 3D image-based point cloud. Finally, by evaluating virtual–real registration for AR on a campus by using the cross-domain image matching results, we demonstrate the feasibility of applying the proposed virtual–real registration to AR in outdoor environments.

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Li, Yinhai, Fei Wang, and Xinhua Hu. "Deep-Learning-Based 3D Reconstruction: A Review and Applications." Applied Bionics and Biomechanics 2022 (September 15, 2022): 1–6. http://dx.doi.org/10.1155/2022/3458717.

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In recent years, deep learning models have been widely used in 3D reconstruction fields and have made remarkable progress. How to stimulate deep academic interest to effectively manage the explosive augmentation of 3D models has been a research hotspot. This work shows mainstream 3D model retrieval algorithm programs based on deep learning currently developed remotely, and further subdivides their advantages and disadvantages according to the behavior evaluation of the algorithm programs obtained by trial. According to other restoration applications, the main 3D model retrieval algorithms can be divided into two categories: (1) 3D standard restoration methods supported by the model, i.e., both the restored object and the recalled object are 3D models. It can be further divided into voxel-based, point coloring-based, and appearance-based methods, and (2) cross-domain 3D model recovery methods supported by 2D replicas, that is, the retrieval motivation is 2D images, and the recovery appearance is 3D models, including retrieval methods supported by 2D display, 2D depiction-based realistic replication and 3D mold recovery methods. Finally, the work proposed novel 3D fashion retrieval algorithms supported by deep science that are analyzed and ventilated, and the unaccustomed directions of future development are prospected.

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Abburu, Sunitha. "Ontology Driven Cross-Linked Domain Data Integration and Spatial Semantic Multi Criteria Query System for Geospatial Public Health." International Journal on Semantic Web and Information Systems 14, no. 3 (July 2018): 1–30. http://dx.doi.org/10.4018/ijswis.2018070101.

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This article describes how public health information management is an interdisciplinary application which deals with cross linked application domains. Geospatial environment, place and meteorology parameters effect public health. Effective decision making plays a vital role and requires disease data analysis which in turn requires effective Public Health Knowledge Base (PHKB) and a strong efficient query engine. Ontologies enhance the performance of the retrieval system and achieve application interoperability. The current research aims at building PHKB through ontology based cross linked domain integration. It designs a dynamic GeoSPARQL query building from simple form based query composition. The spatial semantic multi criteria query engine is developed by identifying all possible query patterns considering the ontology elements and multi criteria from cross linked application domains. The research has adopted OGC, W3C, WHO and mHealth standards.

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Lu, Xin, and Peter James Thomas. "Phase Error Evaluation via Differentiation and Cross-Multiplication Demodulation in Phase-Sensitive Optical Time-Domain Reflectometry." Photonics 10, no. 5 (April 28, 2023): 514. http://dx.doi.org/10.3390/photonics10050514.

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Phase-sensitive optical time-domain reflectometry (φOTDR) is a technology for distributed vibration sensing, where vibration amplitudes are determined by recovering the phase of the backscattered light. Measurement noise induces phase errors, which degrades sensing performance. The phase errors, using a differentiation and cross-multiplication (DCM) algorithm, are investigated theoretically and experimentally in a φOTDR system based on a phase retrieval configuration consisting of an imbalanced Mach–Zehnder interferometer (IMZI) and a 3 × 3 coupler. Analysis shows that phase error is highly dependent on the AC component of the obtained signal, essentially being inversely proportional to the product of the power of the light backscattered from two locations. An analytical expression was derived to estimate the phase error and was confirmed by experiment. When applied to the same measurement data, the error is found to be slightly smaller than that obtained using in-phase/quadrature (I/Q) demodulation. The error, however, increases for longer measurement times.

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Deisboeck, Thomas S., and Jonathan Sagotsky. "Professional Networks in the Life Sciences: Linking the Linked." Cancer Informatics 9 (January 2010): CIN.S5371. http://dx.doi.org/10.4137/cin.s5371.

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The world wide web has furthered the emergence of a multitude of online expert communities. Continued progress on many of the remaining complex scientific questions requires a wide ranging expertise spectrum with access to a variety of distinct data types. Moving beyond peer-to-peer to community-to-community interaction is therefore one of the biggest challenges for global interdisciplinary Life Sciences research, including that of cancer. Cross-domain data query, access, and retrieval will be important innovation areas to enable and facilitate this interaction in the coming years.

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Cheon, Juryong, and Youngjoong Ko. "Parallel sentence extraction to improve cross-language information retrieval from Wikipedia." Journal of Information Science 47, no. 2 (February 10, 2021): 281–93. http://dx.doi.org/10.1177/0165551521992754.

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Translation language resources, such as bilingual word lists and parallel corpora, are important factors affecting the effectiveness of cross-language information retrieval (CLIR) systems. In particular, when large domain-appropriate parallel corpora are not available, developing an effective CLIR system is particularly difficult. Furthermore, creating a large parallel corpus is costly and requires considerable effort. Therefore, we here demonstrate the construction of parallel corpora from Wikipedia as well as improved query translation, wherein the queries are used for a CLIR system. To do so, we first constructed a bilingual dictionary, termed WikiDic. Then, we evaluated individual language resources and combinations of them in terms of their ability to extract parallel sentences; the combinations of our proposed WikiDic with the translation probability from the Web’s bilingual example sentence pairs and WikiDic was found to be best suited to parallel sentence extraction. Finally, to evaluate the parallel corpus generated from this best combination of language resources, we compared its performance in query translation for CLIR to that of a manually created English–Korean parallel corpus. As a result, the corpus generated by our proposed method achieved a better performance than did the manually created corpus, thus demonstrating the effectiveness of the proposed method for automatic parallel corpus extraction. Not only can the method demonstrated herein be used to inform the construction of other parallel corpora from language resources that are readily available, but also, the parallel sentence extraction method will naturally improve as Wikipedia continues to be used and its content develops.

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Nakagawa, Hiroshi. "Disambiguation of single noun translations extracted from bilingual comparable corpora." Terminology 7, no. 1 (December 7, 2001): 63–83. http://dx.doi.org/10.1075/term.7.1.06nak.

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Bilingual machine readable dictionaries are important and indispensable resources of information for cross-language information retrieval, and machine translation. Recently, these cross-language informational activities have begun to focus on specific academic or technological domains. In this paper, we describe a bilingual dictionary acquisition system which extracts translations from non-parallel but comparable corpora of a specific academic domain and disambiguates the extracted translations. The proposed method is two-fold. At the first stage, candidate terms are extracted from a Japanese and English corpus, respectively, and ranked according to their importance as terms. At the second stage, ambiguous translations are resolved by selecting the target language translation which is the nearest in rank to the source language term. Finally, we evaluate the proposed method in an experiment.

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Kaltenthaler, Daniel, Johannes-Y. Lohrer, Florian Richter, and Peer Kröger. "Interdisciplinary knowledge cohesion through distributed information management systems." Journal of Information, Communication and Ethics in Society 16, no. 4 (November 12, 2018): 413–26. http://dx.doi.org/10.1108/jices-03-2018-0021.

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Purpose Interdisciplinary linkage of information is an emerging topic to create knowledge by collaboration of experts in diverse domains. New insights can be found by using the combined techniques and information when people have the chance to discuss and communicate on a common basis. Design/methodology/approach This paper describes RMS Cloud, an information management system which allows distributed data sources to be searched using dynamic joins of results from heterogeneous data formats. It is based on the well-known Mediator architecture, but reverses the connection of the data sources to grant data owners full control over the data. Findings Data owners and learners are enabled to retrieve information and to cross-connect domain-extrinsic knowledge and enhances collaborative learning with a search interface that is intuitive and easy to operate. Originality/value This novel architecture is able to connect to differently shaped data sources from interdisciplinary domains into one common retrieval interface.

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Journal articles on the topic 'Cross-domain retrieval'

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