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5. Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport

Author

Yang, Yang, Fu, Zhao-Yang, Zhan, De-Chuan, Liu, Zhi-Bin, and Jiang, Yuan

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Complex objects are usually with multiple labels, and can be represented by multiple modal representations, e.g., the complex articles contain text and image information as well as multiple annotations. Previous methods assume that the homogeneous multi-modal data are consistent, while in real applications, the raw data are disordered, e.g., the article constitutes with variable number of inconsistent text and image instances. Therefore, Multi-modal Multi-instance Multi-label (M3) learning provides a framework for handling such task and has exhibited excellent performance. However, M3 learning is facing two main challenges: 1) how to effectively utilize label correlation; 2) how to take advantage of multi-modal learning to process unlabeled instances. To solve these problems, we first propose a novel Multi-modal Multi-instance Multi-label Deep Network (M3DN), which considers M3 learning in an end-to-end multi-modal deep network and utilizes consistency principle among different modal bag-level predictions. Based on the M3DN, we learn the latent ground label metric with the optimal transport. Moreover, we introduce the extrinsic unlabeled multi-modal multi-instance data, and propose the M3DNS, which considers the instance-level auto-encoder for single modality and modified bag-level optimal transport to strengthen the consistency among modalities. Thereby M3DNS can better predict label and exploit label correlation simultaneously. Experiments on benchmark datasets and real world WKG Game-Hub dataset validate the effectiveness of the proposed methods.

Published
2021

8. Synthesis of highly stable Ni nanoparticles via electrostatic self-assembly for enhanced hydrogen storage of MgH2.

Author

Tang, Qin-Ke, Liu, Jiang-Chuan, Shi, Rui, Zhu, Yun-Feng, Zhang, Ji-Guang, Liu, Ya-Na, Wang, Jun, Zhang, Yao, Hu, Xiao-Hui, Liu, Zhi-Bin, and Li, Li-Quan

Abstract

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2024
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15. Overview of Development of NPP Advanced Main Control Room Console

Author

Meng, Qing-Jun, Liu, Zhi-Bin, Sun, Yong-Bin, Angrisani, Leopoldo, Series editor, Arteaga, Marco, Series editor, Chakraborty, Samarjit, Series editor, Chen, Jiming, Series editor, Chen, Tan Kay, Series editor, Dillmann, Ruediger, Series editor, Duan, Haibin, Series editor, Ferrari, Gianluigi, Series editor, Ferre, Manuel, Series editor, Hirche, Sandra, Series editor, Jabbari, Faryar, Series editor, Kacprzyk, Janusz, Series editor, Khamis, Alaa, Series editor, Kroeger, Torsten, Series editor, Ming, Tan Cher, Series editor, Minker, Wolfgang, Series editor, Misra, Pradeep, Series editor, Möller, Sebastian, Series editor, Mukhopadhyay, Subhas Chandra, Series editor, Ning, Cun-Zheng, Series editor, Nishida, Toyoaki, Series editor, Panigrahi, Bijaya Ketan, Series editor, Pascucci, Federica, Series editor, Samad, Tariq, Series editor, Seng, Gan Woon, Series editor, Veiga, Germano, Series editor, Wu, Haitao, Series editor, Zhang, Junjie James, Series editor, Xu, Yang, editor, Gao, Feng, editor, Chen, Weihua, editor, Liu, Zheming, editor, and Gu, Pengfei, editor

Published
2018
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