Image Captioning Techniques: A Review

Anbara Z Al-Jamal, Maryam J Bani-Amer, Shadi A. Aljawarneh
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引用次数: 2

Abstract

Image captioning is the process of generating accurate and descriptive captions. As a recently emerged research area, it is attracting more and more attention. To achieve the goal of image captioning, semantic information of images needs to be captured and expressed in natural languages. Image captions need to identify objects, actions, their relationships, and some salient features that may be missing from the image. After identification, the next step is to generate the most relevant and concise image description. This should be syntactically and semantically correct. Deep learning techniques can handle this process with CNNs and LSTMs. In this survey paper, we first talk about techniques used in early work that are mainly retrieval and template-based. Then, we focus on neural network-based techniques, which offer contemporary results. These techniques are in addition divided into subcategories based on the specific framework. Each subcategory is discussed in detail. After that, state-of-the-art methods are compared on benchmark datasets. Following that, discussions on future research approaches are presented.
图像字幕技术综述
图像字幕是生成准确和描述性字幕的过程。作为一个新兴的研究领域,它正受到越来越多的关注。为了实现图像字幕的目标,需要捕获图像的语义信息并用自然语言表达。图像标题需要识别物体、动作、它们之间的关系,以及图像中可能缺少的一些显著特征。识别后,下一步是生成最相关、最简洁的图像描述。这应该在语法和语义上是正确的。深度学习技术可以用cnn和lstm处理这个过程。在这篇调查报告中,我们首先讨论了早期工作中使用的主要是基于检索和模板的技术。然后,我们专注于基于神经网络的技术,它提供了当代的结果。此外,这些技术还根据具体框架划分为子类别。详细讨论了每个子类别。之后,在基准数据集上比较最先进的方法。在此基础上,对今后的研究方向进行了探讨。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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