Deep Adversarial Quantization Network for Cross-Modal Retrieval

Yu Zhou; Yong Feng; Mingliang Zhou; Baohua Qiang; Leong Hou U; Jiajie Zhu

ICASSP 2021accepted0 citations

Deep Adversarial Quantization Network for Cross-Modal Retrieval

Yu Zhou, Yong Feng, Mingliang Zhou, Baohua Qiang, Leong Hou U, Jiajie Zhu

Abstract

In this paper, we propose a seamless multimodal binary learning method for cross-modal retrieval. First, we utilize adversarial learning to learn modality-independent representations of different modalities. Second, we formulate loss function through the Bayesian approach, which aims to jointly maximize correlations of modality-independent representations and learn the common quantizer codebooks for both modalities. Based on the common quantizer codebooks, our method performs efficient and effective cross-modal retrieval with fast distance table lookup. Extensive experiments on three cross-modal datasets demonstrate that our method outperforms state-of-the-art methods. The source code is available at https://github.com/zhouyu1996/DAQN.

BibTeX

@inproceedings{icassp2021_deepadversarialq,
  title = {Deep Adversarial Quantization Network for Cross-Modal Retrieval},
  author = {Yu Zhou and Yong Feng and Mingliang Zhou and Baohua Qiang and Leong Hou U and Jiajie Zhu},
  booktitle = {ICASSP 2021},
  year = {2021}
}