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- W4292794791 abstract "In this paper, we address the problem of cross-modal retrieval in presence of multi-view and multi-label data. For this, we present Multi-view Multi-label Canonical Correlation Analysis (or MVMLCCA), which is a generalization of CCA for multi-view data that also makes use of high-level semantic information available in the form of multi-label annotations in each view. While CCA relies on explicit pairings/associations of samples between two views (or modalities), MVMLCCA uses the available multi-label annotations to establish correspondence across multiple (two or more) views without the need of explicit pairing of multi-view samples. Extensive experiments on two multi-modal datasets demonstrate that the proposed approach offers much more flexibility than the related approaches without compromising on scalability and cross-modal retrieval performance. Our code and precomputed features are available at https://github.com/Rushil231100/MVMLCCA." @default.
- W4292794791 created "2022-08-23" @default.
- W4292794791 creator A5026147694 @default.
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- W4292794791 date "2022-06-01" @default.
- W4292794791 modified "2023-09-27" @default.
- W4292794791 title "Multi-view Multi-label Canonical Correlation Analysis for Cross-modal Matching and Retrieval" @default.
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- W4292794791 doi "https://doi.org/10.1109/cvprw56347.2022.00516" @default.
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