MATEC Web Conf.
Volume 277, 20192018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)
|Number of page(s)||6|
|Section||Data and Signal Processing|
|Published online||02 April 2019|
Semantic representation for visual reasoning
School of Automation, University of Electronic Science and Technology of China, Chengdu, 610054, China
2 Geographical & Sustainability Sciences Department, University of Iowa, Iowa City, IA, 52242, USA
3 Department of Modelling, Simulation, and Visualization Engineering, Old Dominion University, Norfolk, VA 23529, USA
* Corresponding author: firstname.lastname@example.org
In the field of visual reasoning, image features are widely used as the input of neural networks to get answers. However, image features are too redundant to learn accurate characterizations for regular networks. While in human reasoning, abstract description is usually constructed to avoid irrelevant details. Inspired by this, a higher-level representation named semantic representation is introduced in this paper to make visual reasoning more efficient. The idea of the Gram matrix used in the neural style transfer research is transferred here to build a relation matrix which enables the related information between objects to be better represented. The model using semantic representation as input outperforms the same model using image features as input which verifies that more accurate results can be obtained through the introduction of high-level semantic representation in the field of visual reasoning.
© The Authors, published by EDP Sciences, 2019
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