@inproceedings{38a7077b5fdf4ddd92c8df54d7691b35,
title = "Cross-domain and cross-modality transfer learning for multi-domain and multi-modality event detection",
abstract = "Online news media and social media are popular domains for people to acquire real-world event knowledge. In this work, the problem of multi-domain and multi-modality event detection (MMED) is elaborated. We wish to organize the multi-modality data from multiple domains based on real-world events. To this end, a cross-domain and cross-modality transfer learning (CDM) model is proposed. The CDM model aligns the data by exploiting a dictionary-based alignment strategy, and identifies the event labels of the data samples based on the class-specific reconstruction residual. Extensive experiments conducted on real-world data demonstrate the effectiveness of the proposed models. In particular, a benchmark dataset, denoted as MMED100, is released, which can hopefully be used to promote the research on this topic and advance related applications.",
keywords = "Event detection, Multimedia analysis, Social media analytics, Transfer learning",
author = "Zhenguo Yang and Min Cheng and Qing Li and Yukun Li and Zehang Lin and Wenyin Liu",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-68783-4_35",
language = "English",
isbn = "9783319687827",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "516--523",
editor = "Lu Chen and Athman Bouguettaya and Andrey Klimenko and Fedor Dzerzhinskiy and Klimenko, {Stanislav V.} and Xiangliang Zhang and Qing Li and Yunjun Gao and Weijia Jia",
booktitle = "Web Information Systems Engineering – WISE 2017 - 18th International Conference, Proceedings",
note = "18th International Conference on Web Information Systems Engineering, WISE 2017 ; Conference date: 07-10-2017 Through 11-10-2017",
}