Deep image captioning: A review of methods, trends and future challenges

Xu, Liming; Tang, Quan; Lv, Jiancheng; Zheng, Bo; Zeng, Xianhua; Li, Weisheng

doi:10.1016/j.neucom.2023.126287

Cited by 13 publications

(2 citation statements)

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“…The author [8] examines image captioning techniques, historical eras, linguistic customs, benchmark data-sets, evaluation metrics, problems with customized data-sets, real-time efficacy, and potential future developments. In [9] paper examines workflows, feature representation, visual encoding, language generation models, data-sets, and assessments of deep photo captioning approaches in natural and medical sciences. The author [10] assisting the visually impaired, interacting with robots, and video surveillance systems are just a few examples of the many applications that Automatic Visual Captioning (AVC), a deep-learning technology, is utilized in.…”

Section: Overview Of An Earlier Review On Image Captioningmentioning

confidence: 99%

A Comprehensive Survey on Image Captioning for Indian Languages: Techniques, Datasets, and Challenges

Jayaswal,

Rani,

Kaur

2023

Preprint

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In image captioning, we generate visual descriptions from an image. Image Cap-tioning requires identifying the key entity, feature, and association in an image. There is also a requirement to generate captions that are syntactically and semantically correct. The process of image captioning requires computer vision and natural language processing. In the past few decades, a substantial attempt has been made to generate the caption for images. In this survey article, we are going to present an extensive survey on image captioning for Indian Languages. To summarize recent research work in image captioning, first, we briefly review the traditional approach to image captioning depending on template and retrieval. Further deep-learning approaches for image captioning are concentrated which are classified as encoder-decoder architecture, attention-based approach, and transformer architecture. Our main focus in this survey is based on image cap-tioning techniques for Indian languages like Hindi, Bengali Assamese, etc. After that, we analyze the state-of-the-art approach on the most widely dataset i.e. MS COCO dataset with their strengths, limitations, and performance metrics i.e. BLEU, ROUGE, METEOR, CIDEr, SPICE. At last, we explore discussion on open challenges and future direction in the field of image captioning.

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Section: Overview Of An Earlier Review On Image Captioningmentioning

confidence: 99%

A Comprehensive Survey on Image Captioning for Indian Languages: Techniques, Datasets, and Challenges

Jayaswal,

Rani,

Kaur

2023

Preprint

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“…The * symbol indicates that the aspect is partially covered. [27] One Conventional [22] One Systematic [32] One Conventional [23] One Systematic [34] One Conventional [35] One Conventional [30] * One Conventional [28] One Conventional [31] * One Conventional [33] * One Conventional [29] One Conventional ours Two Systematic…”

Section: Human Captioningmentioning

confidence: 99%

A Systematic Literature Review on Using the Encoder-Decoder Models for Image Captioning in English and Arabic Languages

Alsayed,

Arif,

Qadah

et al. 2023

Applied Sciences

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With the explosion of visual content on the Internet, creating captions for images has become a necessary task and an exciting topic for many researchers. Furthermore, image captioning is becoming increasingly important as the number of people utilizing social media platforms grows. While there is extensive research on English image captioning (EIC), studies focusing on image captioning in other languages, especially Arabic, are limited. There has also yet to be an attempt to survey Arabic image captioning (AIC) systematically. This research aims to systematically survey encoder-decoder EIC while considering the following aspects: visual model, language model, loss functions, datasets, evaluation metrics, model comparison, and adaptability to the Arabic language. A systematic review of the literature on EIC and AIC approaches published in the past nine years (2015–2023) from well-known databases (Google Scholar, ScienceDirect, IEEE Xplore) is undertaken. We have identified 52 primary English and Arabic studies relevant to our objectives (The number of articles on Arabic captioning is 11, and the rest are for the English language). The literature review shows that applying the English-specific models to the Arabic language is possible, with the use of a high-quality Arabic database and following the appropriate preprocessing. Moreover, we discuss some limitations and ideas to solve them as a future direction.

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