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Full-Text Articles in Physical Sciences and Mathematics
Query-Document-Dependent Fusion: A Case Study Of Multimodal Music Retrieval, Zhonghua Li, Bingjun Zhang, Yi Yu, Jialie Shen, Ye Wang
Query-Document-Dependent Fusion: A Case Study Of Multimodal Music Retrieval, Zhonghua Li, Bingjun Zhang, Yi Yu, Jialie Shen, Ye Wang
Research Collection School Of Computing and Information Systems
In recent years, multimodal fusion has emerged as a promising technology for effective multimedia retrieval. Developing the optimal fusion strategy for different modality (e.g. content, metadata) has been the subject of intensive research. Given a query, existing methods derive a unified fusion strategy for all documents with the underlying assumption that the relative significance of a modality remains the same across all documents. However, this assumption is often invalid. We thus propose a general multimodal fusion framework, query-document-dependent fusion (QDDF), which derives the optimal fusion strategy for each query-document pair via intelligent content analysis of both queries and documents. By …
Based On Repeated Experience, System For Modification Of Expression And Negating Overload From Media And Optimizing Referential Efficiency, Peter R. Badovinatz, Veronika M. Megler
Based On Repeated Experience, System For Modification Of Expression And Negating Overload From Media And Optimizing Referential Efficiency, Peter R. Badovinatz, Veronika M. Megler
Computer Science Faculty Publications and Presentations
Content items are revealed to a user based on whether they have been previously reviewed by the user. A number of content items are thus received over time. The content items may be discrete content items, or may be portions of a content stream, and may be received over different media. For each content item, it is determined whether the content item was previously reviewed by a user. Where the content item was not previously reviewed, the item is revealed to the user, such as by being displayed or announced to the user. Where the content item was previously reviewed, …
Data Near Here: Bringing Relevant Data Closer To Scientists, Veronika M. Megler, David Maier
Data Near Here: Bringing Relevant Data Closer To Scientists, Veronika M. Megler, David Maier
Computer Science Faculty Publications and Presentations
Large scientific repositories run the risk of losing value as their holdings expand, if it means increased effort for a scientist to locate particular datasets of interest. We discuss the challenges that scientists face in locating relevant data, and present our work in applying Information Retrieval techniques to dataset search, as embodied in the Data Near Here application.