Supervised generative adversarial cross-modal hashing by transferring pairwise similarities for venue discovery
Publication Type
Conference Proceeding Article
Publication Date
9-2019
Abstract
Venue discovery using real-world multimedia data has not been investigated thoroughly. We are referring to business and travel locations as venues in this study and aim to improve the efficiency of venue discovery by hashing. Most existing supervised cross-modal hashing methods map data in different modalities to Hamming space, where the semantic information is exploited to supervise data of different modalities during the training stage. However, previous works neglect pairwise similarity between data in different modalities, which lead to degraded performance of hashing function learning. To address this issue, we propose a supervised Generative Adversarial Cross-modal Hashing method by Transferring Pairwise Similarities (SGACH-TPS). This work has three significant contributions: i) we propose a model for making efficient venue discovery, ii) the supervised generative adversarial network can construct a hash function to map multimodal data to a common hamming space. iii) a simple transfer training strategy for the adversarial network is suggested to supervise data in different modalities where the pairwise similarity is transferred to the fine-tuning stage of training. Evaluation on the new WikiVenue dataset confirms the superiority of the proposed method.
Discipline
Theory and Algorithms
Research Areas
Data Science and Engineering
Publication
Proceedings of the 2019 IEEE 5th International Conference on Multimedia Big Data (BigMM)
Identifier
10.1109/BigMM.2019.000-2
Publisher
IEEE
City or Country
Singapore
Citation
AGGARWAL, Himanshu; SHAH, Rajiv Ratn; TANG, Suhua; and ZHU, Feida.
Supervised generative adversarial cross-modal hashing by transferring pairwise similarities for venue discovery. (2019). Proceedings of the 2019 IEEE 5th International Conference on Multimedia Big Data (BigMM).
Available at: https://ink.library.smu.edu.sg/sis_research/4840
Additional URL
https://doi.org/10.1109/BigMM.2019.000-2