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Fast CNN Pruning via Redundancy-Aware Training Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Two-Stream Convolutional Neural Network for Multimodal Matching

Authors : Youcai Zhang, Yiwei Gu, Xiaodong Gu

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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Abstract

Mulitimudal matching aims to establish relationship across different modalities such as image and text. Existing works mainly focus on maximizing the correlation between feature vectors extracted from the off-the-shelf models. The feature extraction and the matching are two-stage learning process. This paper presents a novel two-stream convolutional neural network that integrates the feature extraction and the matching under an end-to-end manner. Visual and textual stream are designed for feature extraction and then are concatenated with multiple shared layers for multimodal matching. The network is trained using an extreme multiclass classification loss by viewing each multimodal data as a class. Then a finetuning step is performed by a ranking constraint. Experimental results on Flickr30k datasets demonstrate the effectiveness of the proposed network for multimodal matching.

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previous chapter Fast CNN Pruning via Redundancy-Aware Training
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Metadata
Title
Two-Stream Convolutional Neural Network for Multimodal Matching
Authors
Youcai Zhang
Yiwei Gu
Xiaodong Gu
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01418-6_2

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