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- W3121803713 abstract "This paper focuses on the use of embedding with global semantic relations to improve the cross modal retrieval. Our method smoothly bridges the heterogeneity gap by graph embedding and then obtains discriminative representation by supervised learning. First, we construct a semantic correlation graph based on the intra-modal similarity and the semantic propagation of pairwise information. Then, embeddings are learnt from the graph semantic structure which enables all the cross-modal data to be mapped into the same space. Second, based on the previous embeddings, we adopt a simple one-branch neural network to enhance the discrimination of the representation by minimizing the discrimination loss and reconstruction loss. Experimental results on three widely-used benchmark datasets clearly demonstrate the improvement of the proposed approach over the state-of-the-art cross-modal retrieval methods. KeywordsCross-modal retrievalGraph embeddingNeural network" @default.
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- W3121803713 date "2021-01-01" @default.
- W3121803713 modified "2023-10-16" @default.
- W3121803713 title "Improving Supervised Cross-modal Retrieval with Semantic Graph Embedding" @default.
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- W3121803713 doi "https://doi.org/10.1007/978-3-030-67832-6_16" @default.
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