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Title: Cross-Modal Deep Variational Hashing
Authors: Liong, Venice Erin
Lu, Jiwen
Zhou, Jie
Tan, Yap Peng
Keywords: Image Retrieval
Deep Learning
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.
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