Image Caption Generation Using A Deep Architecture

A. Hani, Najiba Tagougui, M. Kherallah
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引用次数: 10

Abstract

Recently, image captioning is a new challenging task that has gathered widespread interest. The task involves generating a concise description of an image in natural language and is currently accomplished by techniques that use a combination of computer vision (CV), natural language processing (NLP), and machine learning methods.In this paper, we presented a model that generates natural language description of an image. We used a combination of convolutional neural networks to extract features and then used recurrent neural networks to generate text from these features. We incorporated the attention mechanism while generating captions. We evaluated the model on MSCOCO database. The obtained results are promising and competitive.
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使用深度架构生成图像标题
近年来,图像字幕是一项具有挑战性的新课题,引起了人们的广泛关注。该任务涉及用自然语言生成图像的简明描述,目前通过结合使用计算机视觉(CV),自然语言处理(NLP)和机器学习方法的技术来完成。在本文中,我们提出了一个生成图像自然语言描述的模型。我们使用卷积神经网络的组合来提取特征,然后使用循环神经网络从这些特征中生成文本。我们在生成字幕时加入了注意力机制。我们在MSCOCO数据库上对模型进行了评估。所得结果具有一定的前景和竞争力。
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