基于Bahdanau注意的孟加拉语图像标题生成

M. M. Alam, M. Rahman, M. Hosen, Khairul Anam Mubin, S. Hossen, M. F. Mridha
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摘要

在过去的几年里,很多工作都是利用图像和机器翻译来进行目标检测的。受这些作品的启发,我们引入了基于Bahdanau注意力的孟加拉语图像标题生成(BABBICG),它可以根据图像自动生成孟加拉语标题。传统的编码器-解码器体系结构的性能缺陷将随着巴赫达瑙关注的减少而得到显著改善。在这项工作中,我们使用InceptionV3神经网络从图像中提取特征,并使用RNN解码器生成标题。我们使用门控循环单元(GRU)方法作为RNN。我们使用Mendeley Data的BanglaLekhaImageCaptions数据集来评估模型,该数据集可以帮助生成孟加拉语标题。
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Bahdanau Attention Based Bengali Image Caption Generation
In the past few years, many works are done in object detection using images and machine translation. Inspired by those works we introduced Bahdanau Attention Based Bengali Image Caption Generation (BABBICG) that generate automatically bangla caption based on images. The Conventional encoder-decoder architectures performance curse will reduce by Bahdanau Attention and achieving momentous improvements over encoder-decoder architectures. In this work, we extract features from images using InceptionV3 neural network and generate caption using RNN decoder. We used Gated Recurrent Unit (GRU) approach as RNN. We evaluate the model using BanglaLekhaImageCaptions dataset from Mendeley Data that can help to generate bangla caption.
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