| Paper ID | MMSP-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 |
| Session | MMSP-8: Multimedia Retrieval and Signal Detection |
| Location | Gather.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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
