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|Title:||Cross-Modal Deep Variational Hashing||Authors:||Liong, Venice Erin
Tan, Yap Peng
|Issue Date:||2017||Source:||Liong, V. E., Lu, J., Tan, Y.-P., & Zhou, J. (2017). Cross-Modal Deep Variational Hashing. 2017 IEEE International Conference on Computer Vision (ICCV 17), 4077-4085.||Abstract:||In this paper, we propose a cross-modal deep variational hashing (CMDVH) method to learn compact binary codes for cross-modality multimedia retrieval. Unlike most existing cross-modal hashing methods which learn a single pair of projections to map each example into a binary vector, we design a deep fusion neural network to learn non-linear transformations from image-text input pairs, such that a unified binary code is achieved in a discrete and discriminative manner using a classification-based hinge-loss criterion. We then design modality-specific neural networks in a probabilistic manner such that we model a latent variable to be close as possible from the inferred binary codes, at the same time approximated by a posterior distribution regularized by a known prior, which is suitable for out-of-sample extension. Experimental results on three benchmark datasets show the efficacy of the proposed approach.||URI:||https://hdl.handle.net/10356/85091
|Rights:||© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://openaccess.thecvf.com/content_iccv_2017/html/Liong_Cross-Modal_Deep_Variational_ICCV_2017_paper.html].||metadata.item.grantfulltext:||open||metadata.item.fulltext:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
IGS Conference Papers
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