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- W2531675257 abstract "We argue that cross-modal classification, where models are trained on data from one modality (e.g. text) and applied to data from another (e.g. image), is a relevant problem in multimedia retrieval. We propose a method that addresses this specific problem, related to but different from cross-modal retrieval and bimodal classification. This method relies on a common latent space where both modalities have comparable representations and on an auxiliary dataset from which we build a more complete bimodal representation of any unimodal data. Evaluations on Pascal VOC07 and NUS-WIDE show that the novel representation method significantly improves the results compared to the use of a latent space alone. The level of performance achieved makes cross-modal classification a convincing choice for real applications." @default.
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- W2531675257 date "2016-10-16" @default.
- W2531675257 modified "2023-09-25" @default.
- W2531675257 title "Cross-modal Classification by Completing Unimodal Representations" @default.
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- W2531675257 doi "https://doi.org/10.1145/2983563.2983570" @default.
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