2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMMSP-8.1
Paper Title DEEP ADVERSARIAL QUANTIZATION NETWORK FOR CROSS-MODAL RETRIEVAL
Authors Yu Zhou, Yong Feng, Chongqing University, China; Mingliang Zhou, University of Macau, China; Baohua Qiang, Guilin University of Electronic Technology, China; Leong Hou U, University of Macau, China; Jiajie Zhu, Chongqing University, China
SessionMMSP-8: Multimedia Retrieval and Signal Detection
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Multimedia Signal Processing: Multimedia Databases and File Systems
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.