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Learning deep representation of multityped objects and tasks

Version 2 2024-06-03, 17:51
Version 1 2023-10-25, 23:22
journal contribution
posted on 2024-06-03, 17:51 authored by Truyen TranTruyen Tran, Dinh Phung, Svetha VenkateshSvetha Venkatesh
We introduce a deep multitask architecture to integrate multityped representations of multimodal objects. This multitype exposition is less abstract than the multimodal characterization, but more machine-friendly, and thus is more precise to model. For example, an image can be described by multiple visual views, which can be in the forms of bag-of-words (counts) or color/texture histograms (real-valued). At the same time, the image may have several social tags, which are best described using a sparse binary vector. Our deep model takes as input multiple type-specific features, narrows the cross-modality semantic gaps, learns cross-type correlation, and produces a high-level homogeneous representation. At the same time, the model supports heterogeneously typed tasks. We demonstrate the capacity of the model on two applications: social image retrieval and multiple concept prediction. The deep architecture produces more compact representation, naturally integrates multiviews and multimodalities, exploits better side information, and most importantly, performs competitively against baselines.

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arXiv

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CN Other journal article

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Cornell University

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