A thorough review of models, evaluation metrics, and datasets on image captioning. Issue 2 (22nd November 2021)

• Record Type: Journal Article
• Authors: Luo, Gaifang; Cheng, Lijun; Jing, Chao; Zhao, Can; Song, Guozhu
• Is Part Of: IET image processing. Volume 16:Issue 2(2022)
• Page Start: 311
• Page End: 332
• Publication Date: 2021-11-22
• Subjects: Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12367
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: Image captioning means generate descriptive sentences from a query image automatically. It has recently received widespread attention from the computer vision and natural language processing communities as an emerging visual task. Currently, both components have evolved considerably by exploiting object regions, attributes, attention mechanism methods, entity recognition with novelties, and training strategies. However, despite the impressive results, the research has not yet come to a conclusive answer. This survey aims to provide a comprehensive overview of image captioning methods, from technical architectures to benchmark datasets, evaluation metrics, and comparison of state‐of‐the‐art methods. In particular, image captioning methods are divided into different categories based on the technique adopted. Representative methods in each class are summarized, and their advantages and limitations are discussed. Moreover, many related state‐of‐the‐art studies were quantitatively compared to determine the recent trends and future directions in image captioning. The ultimate goal of this work is to serve as a tool for understanding the existing literature and highlighting future directions in the area of image captioning for Computer Vision and Natural Language Processing communities may benefit from.