Cross-Modal Retrieval With CNN Visual Features: A New Baseline

IEEE Transactions on Cybernetics
Yunchao WeiShuicheng Yan

Abstract

Recently, convolutional neural network (CNN) visual features have demonstrated their powerful ability as a universal representation for various recognition tasks. In this paper, cross-modal retrieval with CNN visual features is implemented with several classic methods. Specifically, off-the-shelf CNN visual features are extracted from the CNN model, which is pretrained on ImageNet with more than one million images from 1000 object categories, as a generic image representation to tackle cross-modal retrieval. To further enhance the representational ability of CNN visual features, based on the pretrained CNN model on ImageNet, a fine-tuning step is performed by using the open source Caffe CNN library for each target data set. Besides, we propose a deep semantic matching method to address the cross-modal retrieval problem with respect to samples which are annotated with one or multiple labels. Extensive experiments on five popular publicly available data sets well demonstrate the superiority of CNN visual features for cross-modal retrieval.

References

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Sep 24, 2014·IEEE Transactions on Cybernetics·Meng WangShuicheng Yan
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Citations

Jul 12, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jufeng YangMing-Hsuan Yang
Sep 9, 2017·IEEE Transactions on Cybernetics·Guanqun CaoMoncef Gabbouj
Jul 12, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Yuxin PengYuxin Yuan
Dec 20, 2016·IEEE Transactions on Cybernetics·Hanli WangSam Kwong
Feb 7, 2017·IEEE Transactions on Cybernetics·Xiao ZengChun Qi
Dec 4, 2016·IEEE Transactions on Cybernetics·Yong ZhangMahardhika Pratama
Aug 15, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Lingyun SongSamar Abbas

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