Multimodal Encoders and Decoders with Gate Attention for Visual Question Answering

Haiyan Li1 and Dezhi Han1

  1. School of Information Engineering, Shanghai Maritime University
    Shanghai,201306, China,


Visual Question Answering (VQA) is a multimodal research related to Computer Vision (CV) and Natural Language Processing (NLP). How to better obtain useful information from images and questions and give an accurate answer to the question is the core of the VQA task. This paper presents a VQA model based on multimodal encoders and decoders with gate attention (MEDGA). Each encoder and decoder block in the MEDGA applies not only self-attention and crossmodal attention but also gate attention, so that the new model can better focus on inter-modal and intra-modal interactions simultaneously within visual and language modality. Besides, MEDGA further filters out noise information irrelevant to the results via gate attention and finally outputs attention results that are closely related to visual features and language features, which makes the answer prediction result more accurate. Experimental evaluations on the VQA 2.0 dataset and the ablation experiments under different conditions prove the effectiveness of MEDGA. In addition, the MEDGA accuracy on the test-std dataset has reached 70.11%, which exceeds many existing methods.

Key words

Deep Learning, Artificial Intelligence, Visual Question Answering, Gate Attention, Multimodal Learning

Digital Object Identifier (DOI)

Publication information

Volume 18, Issue 3 (June 2021)
Year of Publication: 2021
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Li, H., Han, D.: Multimodal Encoders and Decoders with Gate Attention for Visual Question Answering. Computer Science and Information Systems, Vol. 18, No. 3, 1023–1040. (2021),