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Cross-Modal Event Retrieval: A Dataset and a Baseline Using Deep Semantic Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11165)

Abstract

In this paper, we propose to learn Deep Semantic Space (DSS) for cross-modal event retrieval, which is achieved by exploiting deep learning models to extract semantic features from images and textual articles jointly. More specifically, a VGG network is used to transfer deep semantic knowledge from a large-scale image dataset to the target image dataset. Simultaneously, a fully-connected network is designed to model semantic representation from textual features (e.g., TF-IDF, LDA). Furthermore, the obtained deep semantic representations for image and text can be mapped into a high-level semantic space, in which the distance between data samples can be measured straightforwardly for cross-model event retrieval. In particular, we collect a dataset called Wiki-Flickr event dataset for cross-modal event retrieval, where the data are weakly aligned unlike image-text pairs in the existing cross-modal retrieval datasets. Extensive experiments conducted on both the Pascal Sentence dataset and our Wiki-Flickr event dataset show that our DSS outperforms the state-of-the-art approaches.

Keywords

Cross-modal event retrieval Deep learning Common space 

Notes

Acknowledgments

The authors would like to thank Zehang Lin and Feitao Huang for data collection. This work is supported by the National Natural Science Foundation of China (No. 61703109, No. 91748107, No. U1611461), the Guangdong Innovative Research Team Program (No. 2014ZT05G157), Science and Technology Program of Guangdong Province, China (No. 2016A010101012), and CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, 100190, Beijing, China (No. CASNDST201703), and an internal grant from City University of Hong Kong (Project No. 9610367).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyGuangdong University of TechnologyGuangzhouChina
  2. 2.School of Computer Science and EngineeringSouth China University of TechnologyGuangzhouChina
  3. 3.Department of Computer ScienceCity University of Hong KongHong KongChina

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