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Thematische Bibliographien / Multi-Step Retrieval

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Auswahl der wissenschaftlichen Literatur zum Thema „Multi-Step Retrieval“

Autor: Grafiati

Veröffentlicht am 7. Juni 2025

Zuletzt aktualisiert am 2. August 2025

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Zeitschriftenartikel zum Thema "Multi-Step Retrieval"

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Okamura, Rintaro, Hironobu Iwabuchi, and K. Sebastian Schmidt. "Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning." Atmospheric Measurement Techniques 10, no. 12 (2017): 4747–59. http://dx.doi.org/10.5194/amt-10-4747-2017.

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Abstract. Three-dimensional (3-D) radiative-transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. The challenge is that 3-D effects manifest themselves across multiple satellite pixels, which traditional single-pixel approaches cannot capture. In this study, we present two multi-pixel retrieval approaches based on deep learning, a technique that is becoming increasingly successful for complex problems in engineering and other areas. Specifically, we use deep neural networks (DNNs) to obtain multi-pixel estimates of cloud optical thickness

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Wang, Yongyue, Beitong Yao, Tianbo Wang, Chunhe Xia, and Xianghui Zhao. "A Cognitive Method for Automatically Retrieving Complex Information on a Large Scale." Sensors 20, no. 11 (2020): 3057. http://dx.doi.org/10.3390/s20113057.

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Modern retrieval systems tend to deteriorate because of their large output of useless and even misleading information, especially for complex search requests on a large scale. Complex information retrieval (IR) tasks requiring multi-hop reasoning need to fuse multiple scattered text across two or more documents. However, there are two challenges for multi-hop retrieval. To be specific, the first challenge is that since some important supporting facts have little lexical or semantic relationship with the retrieval query, the retriever often omits them; the second challenge is that once a retrie

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Zalach, Jacob, Christian von Savigny, Arvid Langenbach, Gerd Baumgarten, Franz-Josef Lübken, and Adam Bourassa. "A Method for Retrieving Stratospheric Aerosol Extinction and Particle Size from Ground-Based Rayleigh-Mie-Raman Lidar Observations." Atmosphere 11, no. 8 (2020): 773. http://dx.doi.org/10.3390/atmos11080773.

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We report on the retrieval of stratospheric aerosol particle size and extinction coefficient profiles from multi-color backscatter measurements with the Rayleigh–Mie–Raman lidar operated at the Arctic Lidar Observatory for Middle Atmosphere Research (ALOMAR) in northern Norway. The retrievals are based on a two-step approach. In a first step, the median radius of an assumed monomodal log-normal particle size distribution with fixed width is retrieved based on a color index formed from the measured backscatter ratios at the wavelengths of 1064 nm and 532 nm. An intrinsic ambiguity of the retrie

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Huang, Jie, Mo Wang, Yunpeng Cui, et al. "Layered Query Retrieval: An Adaptive Framework for Retrieval-Augmented Generation in Complex Question Answering for Large Language Models." Applied Sciences 14, no. 23 (2024): 11014. http://dx.doi.org/10.3390/app142311014.

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Retrieval-augmented generation (RAG) addresses the problem of knowledge cutoff and overcomes the inherent limitations of pre-trained language models by retrieving relevant information in real time. However, challenges related to efficiency and accuracy persist in current RAG strategies. A key issue is how to select appropriate methods for user queries of varying complexity dynamically. This study introduces a novel adaptive retrieval-augmented generation framework termed Layered Query Retrieval (LQR). The LQR framework focuses on query complexity classification, retrieval strategies, and relev

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Long, Xinwei, Jiali Zeng, Fandong Meng, et al. "Generative Multi-Modal Knowledge Retrieval with Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 18733–41. http://dx.doi.org/10.1609/aaai.v38i17.29837.

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Knowledge retrieval with multi-modal queries plays a crucial role in supporting knowledge-intensive multi-modal applications. However, existing methods face challenges in terms of their effectiveness and training efficiency, especially when it comes to training and integrating multiple retrievers to handle multi-modal queries. In this paper, we propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases, even when trained with limited data.

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Hayashi, Hiroya, Akihiro Tsuji, Ryotaro Asano, et al. "Successful multi-step catheter intervention for thrombotic inferior vena cava filter retrieval." Journal of Cardiology Cases 20, no. 4 (2019): 142–46. http://dx.doi.org/10.1016/j.jccase.2019.07.006.

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Shieh, Jyh-Ren, Ching-Yung Lin, Shun-Xuan Wang, and Ja-Ling Wu. "Building Multi-Modal Relational Graphs for Multimedia Retrieval." International Journal of Multimedia Data Engineering and Management 2, no. 2 (2011): 19–41. http://dx.doi.org/10.4018/jmdem.2011040102.

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The abundance of Web 2.0 social media in various media formats calls for integration that takes into account tags associated with these resources. The authors present a new approach to multi-modal media search, based on novel related-tag graphs, in which a query is a resource in one modality, such as an image, and the results are semantically similar resources in various modalities, for instance text and video. Thus the use of resource tagging enables the use of multi-modal results and multi-modal queries, a marked departure from the traditional text-based search paradigm. Tag relation graphs

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Tu, Yunbin, Liang Li, Li Su, and Qingming Huang. "Query-centric Audio-Visual Cognition Network for Moment Retrieval, Segmentation and Step-Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 7 (2025): 7464–72. https://doi.org/10.1609/aaai.v39i7.32803.

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Video has emerged as a favored multimedia format on the internet. To better gain video contents, a new topic HIREST is presented, including video retrieval, moment retrieval, moment segmentation, and step-captioning. The pioneering work chooses the pre-trained CLIP-based model for video retrieval, and leverages it as a feature extractor for other three challenging tasks solved in a multi-task learning paradigm. Nevertheless, this work struggles to learn the comprehensive cognition of user-preferred content, due to disregarding the hierarchies and association relations across modalities. In thi

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Khot, Tushar, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. "QASC: A Dataset for Question Answering via Sentence Composition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8082–90. http://dx.doi.org/10.1609/aaai.v34i05.6319.

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Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relat

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Singh, Vibhav Prakash, Rajeev Srivastava, Yadunath Pathak, Shailendra Tiwari, and Kuldeep Kaur. "Content-based image retrieval based on supervised learning and statistical-based moments." Modern Physics Letters B 33, no. 19 (2019): 1950213. http://dx.doi.org/10.1142/s0217984919502130.

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Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patte

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Buchteile zum Thema "Multi-Step Retrieval"

1

Sarmento, Luís. "A First Step to Address Biography Generation as an Iterative QA Task." In Evaluation of Multilingual and Multi-modal Information Retrieval. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74999-8_56.

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2

Ougouti, Naïma Souâd, Haféda Belbachir, and Youssef Amghar. "Semantic Mediation in MedPeer." In Information Retrieval and Management. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5191-1.ch098.

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Peer-to-Peer (P2P) infrastructure is an emerging paradigm that offers new opportunities for the development of large-scale distributed systems. This architecture combined with the new techniques introduced by semantic web as ontologies encouraged the emergence of new multi-source data integration possibilities for sharing information. A challenging problem in such systems is to find correspondences between concepts of their different ontologies. This is a necessary step before locating peers that are relevant with respect to a given query. In this paper, the authors propose a new ontology alig

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Shieh, Jyh-Ren, Ching-Yung Lin, Shun-Xuan Wang, and Ja-Ling Wu. "Building Multi-Modal Relational Graphs for Multimedia Retrieval." In Multimedia Data Engineering Applications and Processing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2940-0.ch009.

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The abundance of Web 2.0 social media in various media formats calls for integration that takes into account tags associated with these resources. The authors present a new approach to multi-modal media search, based on novel related-tag graphs, in which a query is a resource in one modality, such as an image, and the results are semantically similar resources in various modalities, for instance text and video. Thus the use of resource tagging enables the use of multi-modal results and multi-modal queries, a marked departure from the traditional text-based search paradigm. Tag relation graphs

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4

V, Jalajakshi, and Myna A N. "Feature Extraction for Big Data Using AI." In Artificial Intelligence and Communication Technologies, 2023rd ed. Soft Computing Research society, 2023. http://dx.doi.org/10.52458/978-81-955020-5-9-97.

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Internet around the world is producing loads of data every second in different ways, the speed of this information is being spread across the corners of the globe in very few seconds. A multi-feature information retrieval approach is utilized, and predicated on this, an ai - powered big data MFE scheme is intended, with the regular news framework as an application example, where it would be expanded, and necessary analysis is performed. This method is taken to the algorithm design of hot event identification using a news article as the sender. As a result, a twostage multi-function fusion clus

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Germer, Sebastian, Christiane Rudolph, Alexander Katalinic, Natalie Rath, Katharina Rausch, and Heinz Handels. "Lung Cancer Survival Estimation Using Data from Seven German Cancer Registries." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250379.

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Predicting the survival of cancer patients is of high importance for the medical community, e.g. for evaluating therapy strategies. This study is based on lung cancer data retrieved from seven German cancer registries according to the German basic oncology dataset. After data integration and preprocessing, we predicted the survival for 6, 12, 18 and 24 months respectively using a gradient boosting algorithm. To gain insight into the decision process of the models, we identified the features that have a high impact on patient survival using permutation feature importance scores as explainabilit

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Cappello, Alice, Sara Mora, Daniele Roberto Giacobbe, Matteo Bassetti, and Mauro Giacomini. "Defining a Preprocessing Pipeline for the MULTI-SITA Project and General Medical Italian Natural Language Data." In Telehealth Ecosystems in Practice. IOS Press, 2023. http://dx.doi.org/10.3233/shti230737.

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The application of Natural Language Processing (NLP) to medical data has revolutionized different aspects of health care. The benefits obtained from the implementation of this technique spill over into several areas, including in the implementation of chatbots, which can provide medical assistance remotely. Every possible application of NLP depends on one first main step: the pre-processing of the corpus retrieved. The raw data must be prepared with the aim to be used efficiently for further analysis. Considerable progress has been made in this direction for the English language but for other

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Konferenzberichte zum Thema "Multi-Step Retrieval"

1

Ma, Xishan, Yushui Geng, Jing Zhao, and Huanxiao Zhou. "Semantic Information Reasoning and Multi-Step Cross-Modal Interaction Network for Image-Text Retrieval." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650589.

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2

Das, Rajarshi, Ameya Godbole, Dilip Kavarthapu, et al. "Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering." In Proceedings of the 2nd Workshop on Machine Reading for Question Answering. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-5816.

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3

Zhao, Chen, Chenyan Xiong, Jordan Boyd-Graber, and Hal Daumé III. "Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval." In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.naacl-main.368.

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4

Danielczuk, Michael, Andrey Kurenkov, Ashwin Balakrishna, et al. "Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794143.

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5

Liao, Hao, Jiahao Peng, Zhanyi Huang, et al. "MUSER: A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2023. http://dx.doi.org/10.1145/3580305.3599873.

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6

Wang, Wenzhe, Mengdan Zhang, Runnan Chen, et al. "Dig into Multi-modal Cues for Video Retrieval with Hierarchical Alignment." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/154.

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Multi-modal cues presented in videos are usually beneficial for the challenging video-text retrieval task on internet-scale datasets. Recent video retrieval methods take advantage of multi-modal cues by aggregating them to holistic high-level semantics for matching with text representations in a global view. In contrast to this global alignment, the local alignment of detailed semantics encoded within both multi-modal cues and distinct phrases is still not well conducted. Thus, in this paper, we leverage the hierarchical video-text alignment to fully explore the detailed diverse characteristic

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Trivedi, Harsh, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. "Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.557.

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8

Zhou, Yan, Jie Guo, Hao Sun, Bin Song, and Fei Richard Yu. "Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation." In SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2023. http://dx.doi.org/10.1145/3539618.3591950.

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9

Yu, Zheng, Wenmin Wang, and Ge Li. "Multi-step Self-attention Network for Cross-modal Retrieval Based on a Limited Text Space." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682424.

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10

Zhao, Zhou, Xinghua Jiang, Deng Cai, Jun Xiao, Xiaofei He, and Shiliang Pu. "Multi-Turn Video Question Answering via Multi-Stream Hierarchical Attention Context Network." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/513.

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Conversational video question answering is a challenging task in visual information retrieval, which generates the accurate answer from the referenced video contents according to the visual conversation context and given question. However, the existing visual question answering methods mainly tackle the problem of single-turn video question answering, which may be ineffectively applied for multi-turn video question answering directly, due to the insufficiency of modeling the sequential conversation context. In this paper, we study the problem of multi-turn video question answering from the vie

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