کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11012485 | 1798846 | 2018 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Image captioning with triple-attention and stack parallel LSTM
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Image captioning aims to describe the content of images with a sentence. It is a natural way for people to express their understanding, but a challenging and important task from the view of image understanding. In this paper, we propose two innovations to improve the performance of such a sequence learning problem. First, we give a new attention method named triple attention (TA-LSTM) which can leverage the image context information at every stage of LSTM. Then, we redesign the structure of basic LSTM, in which not only the stacked LSTM but also the paralleled LSTM are adopted, called as PS-LSTM. In this structure, we not only use the stack LSTM but also use the parallel LSTM to achieve the improvement of the performance compared with the normal LSTM. Through this structure, the proposed model can ensemble more parameters on single model and has ensemble ability itself. Through numerical experiments, on the public available MSCOCO dataset, our final TA-PS-LSTM model achieves comparable performance with some state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 319, 30 November 2018, Pages 55-65
Journal: Neurocomputing - Volume 319, 30 November 2018, Pages 55-65
نویسندگان
Xinxin Zhu, Lixiang Li, Jing Liu, Ziyi Li, Haipeng Peng, Xinxin Niu,