使用大数据和深度学习的多语言图像标题生成器

Naresh Grover, Anchita Singh, Suganeshwari G
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引用次数: 1

摘要

自动图像字幕旨在生成关于图片的描述性句子。对于这个任务,我们正在创建一个模型,当输入图像作为描述图像主题的输入时,该模型将吐出一个英语句子。近年来,认知计算领域的科学家们对其给予了极大的关注。这项工作具有挑战性,因为它需要融合自然语言处理和计算机视觉这两个不同但相关的学科的思想。利用CNN和LSTM的集成,我们开发了一个生成图像标题的模型。卷积神经网络和长短期记忆模型背后的思想被结合起来创建了这个模型。卷积神经网络作为编码器,从图像中提取信息。与此同时,长短期记忆负责解码器的角色,想出单词来描述图像。当数据集非常大时,问题就出现了,并且系统只需要CPU支持就需要花费数周的时间来训练网络,以减少所需的时间
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multilingual Image caption Generator using Big data and Deep Learning
Automatic image captioning aims to produce a descriptive sentence about a picture. For this task, we are creating a model that will spit out an English sentence when an image is given as input describing the image’s subject. Scien-tists in the field of cognitive computing have paid much attention to it in recent years. The endeavor is challenging because it requires merging ideas from two distinct but related disciplines: natural language processing and computer vision. Using the integration of CNN with LSTM, we developed a model for generating image captions. The ideas behind a Convolutional Neural Network and a Long Short-Term Memory model were combined to create this model. The convolutional neural network serves as the encoder, extracting information from images. At the same time, the long short-term memory is responsible for the decoder role, coming up with words to describe the image. The problem arises when the dataset is significant, and it takes weeks for systems to have only CPU support to train the network to decrease the time it is required to
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