With the advent of abundant multimedia data on the Internet, there have been research efforts on multimodal machine learning to utilize data from different modalities. Current approaches mostly focus on developing models to fuse low-level features from multiple modalities and learn unified representation from different modalities. But most related work failed to justify why we should use multimodal data and multimodal fusion, and few of them leveraged the complementary relation among different modalities.
In this paper, we first identify the correlative and complementary relations among multiple modalities. Then we propose a probabilistic ensemble fusion model to capture the complementary relation between two modalities (images and text). Experimental results on the UIUC-ISD dataset show our ensemble approach outperforms approaches using only single modality. Word sense disambiguation (WSD) is the use case we studied to demonstrate the effectiveness of our probabilistic ensemble fusion model.
Authors:
Yang Peng, Daisy Zhe Wang, Ishan Patwa, Dihong Gong, Chunsheng Victor Fang
Bibtex:
@inproceedings{peng2015probabilistic,
title={Probabilistic Ensemble Fusion for Multimodal Word Sense Disambiguation},
author={Peng, Yang and Wang, Daisy Zhe and Patwa, Ishan and Gong, Dihong and Fang, Chunsheng Victor},
booktitle={2015 IEEE International Symposium on Multimedia (ISM)},
pages={172--177},
year={2015},
organization={IEEE}
}
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