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Stacked Attention Networks for Referring Expressions Comprehension

Yugang Li1, *, Haibo Sun1, Zhe Chen1, Yudan Ding1, Siqi Zhou2

1 Academy of Broadcasting Science, Beijing, 100866, China.
2 School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore.

* Corresponding Author: Yugang Li. Email: email.

Computers, Materials & Continua 2020, 65(3), 2529-2541. https://doi.org/10.32604/cmc.2020.011886

Abstract

Referring expressions comprehension is the task of locating the image region described by a natural language expression, which refer to the properties of the region or the relationships with other regions. Most previous work handles this problem by selecting the most relevant regions from a set of candidate regions, when there are many candidate regions in the set these methods are inefficient. Inspired by recent success of image captioning by using deep learning methods, in this paper we proposed a framework to understand the referring expressions by multiple steps of reasoning. We present a model for referring expressions comprehension by selecting the most relevant region directly from the image. The core of our model is a recurrent attention network which can be seen as an extension of Memory Network. The proposed model capable of improving the results by multiple computational hops. We evaluate the proposed model on two referring expression datasets: Visual Genome and Flickr30k Entities. The experimental results demonstrate that the proposed model outperform previous state-of-the-art methods both in accuracy and efficiency. We also conduct an ablation experiment to show that the performance of the model is not getting better with the increase of the attention layers.

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APA Style
Li, Y., Sun, H., Chen, Z., Ding, Y., Zhou, S. (2020). Stacked attention networks for referring expressions comprehension. Computers, Materials & Continua, 65(3), 2529-2541. https://doi.org/10.32604/cmc.2020.011886
Vancouver Style
Li Y, Sun H, Chen Z, Ding Y, Zhou S. Stacked attention networks for referring expressions comprehension. Comput Mater Contin. 2020;65(3):2529-2541 https://doi.org/10.32604/cmc.2020.011886
IEEE Style
Y. Li, H. Sun, Z. Chen, Y. Ding, and S. Zhou "Stacked Attention Networks for Referring Expressions Comprehension," Comput. Mater. Contin., vol. 65, no. 3, pp. 2529-2541. 2020. https://doi.org/10.32604/cmc.2020.011886



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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