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Heterogeneous gap among different modalities emerges as one of the criticalissues in modern AI problems. Unlike traditional uni-modal cases, where rawfeatures are extracted and directly measured, the heterogeneous nature of crossmodal tasks requires the intrinsic semantic representation to be compared in aunified framework. This paper studies the learning of different representationsthat can be retrieved across different modality contents. A novel approach formining cross-modal representations is proposed by incorporating explicit linearsemantic projecting in Hilbert space. The insight is that the discriminativestructures of different modality data can be linearly represented inappropriate high dimension Hilbert spaces, where linear operations can be usedto approximate nonlinear decisions in the original spaces. As a result, anefficient linear semantic down mapping is jointly learned for multimodal data,leading to a common space where they can be compared. The mechanism of "featureup-lifting and down-projecting" works seamlessly as a whole, which accomplishescrossmodal retrieval tasks very well. The proposed method, named as shareddiscriminative semantic representation learning (\textbf{SDSRL}), is tested ontwo public multimodal dataset for both within- and inter- modal retrieval. Theexperiments demonstrate that it outperforms several state-of-the-art methods inmost scenarios.

Learning Discriminative Representations for Semantic Cross Media Retrieval