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• W2783179304 abstract "Recent advances in deep learning and distributed representations of images and text have resulted in the emergence of several neural architectures for cross-modal retrieval tasks, such as searching collections of images in response to textual queries and assigning textual descriptions to images. However, the multi-modal retrieval scenario, when a query can be either a text or an image and the goal is to retrieve both a textual fragment and an image, which should be considered as an atomic unit, has been significantly less studied. In this paper, we propose a gated neural architecture to project image and keyword queries as well as multi-modal retrieval units into the same low-dimensional embedding space and perform semantic matching in this space. The proposed architecture is trained to minimize structured hinge loss and can be applied to both cross- and multi-modal retrieval. Experimental results for six different cross- and multi-modal retrieval tasks obtained on publicly available datasets indicate superior retrieval accuracy of the proposed architecture in comparison to the state-of-art baselines." @default.
• W2783179304 created "2018-01-26" @default.
• W2783179304 creator A5025168533 @default.
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• W2783179304 date "2018-02-02" @default.
• W2783179304 modified "2023-10-18" @default.
• W2783179304 title "Deep Neural Architecture for Multi-Modal Retrieval based on Joint Embedding Space for Text and Images" @default.
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• W2783179304 doi "https://doi.org/10.1145/3159652.3159735" @default.
• W2783179304 hasPublicationYear "2018" @default.
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