Temporal Convolutional and Recurrent Networks for Image Captioning

被引：0
|
作者：
Iskra, Natalia [1 ]
Iskra, Vitaly [2 ]
机构：
[1] Belarusian State Univ Informat & Radioelect, Minsk, BELARUS
[2] Omnigon Commun LLC, New York, NY USA
来源：
PATTERN RECOGNITION AND INFORMATION PROCESSING, PRIP 2019 | 2019年 / 1055卷
关键词：
Image captioning; Convolutional neural networks; Recurrent neural networks; Visual Genome; Dilated convolution; Weight normalization; Dropout; Adam optimization;
D O I：
10.1007/978-3-030-35430-5_21
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Recently temporal convolutional networks have shown excellent qualities in sequence modeling tasks [1]. Taking this fact into account, in this paper we investigate the possibilities of replacing recurrent networks in architectures targeted specifically at image captioning. We evaluate the solution on Visual Genome dataset [2], which provides extensive set of labels and descriptions that thoroughly grounds visual concepts to natural language.
引用
收藏
页码：254 / 266
页数：13
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