The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A survey on automatic image caption generation
Abstract
Abstract Image captioning means automatically generating a caption for an image. 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. Connecting both research communities of computer vision and natural language processing, image captioning is a quite challenging task. Various approaches have been proposed to solve this problem. In this paper, we present a survey on advances in image captioning research. Based on the technique adopted, we classify image captioning approaches into different categories. Representative methods in each category are summarized, and their strengths and limitations are talked about. In this paper, we first discuss methods used in early work which are mainly retrieval and template based. Then, we focus our main attention on neural network based methods, which give state of the art results. Neural network based methods are further divided into subcategories based on the specific framework they use. Each subcategory of neural network based methods are discussed in detail. After that, state of the art methods are compared on benchmark datasets. Following that, discussions on future research directions are presented.
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Cites
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Cited by
A Comprehensive Survey of Deep Learning for Image Captioning
Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019) · ACM Computing Surveys · doi:10.1145/3295748 · Cited by 879
“So far, only three survey papers [8, 13, 75] have been published on this research topic.”
BackgroundFrom Show to Tell: A Survey on Deep Learning-Based Image Captioning
Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., & Cucchiara, R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3148210 · Cited by 373
Deep learning in multi-object detection and tracking: state of the art
Pal, S. K., Pramanik, A., Maiti, J., & Mitra, P. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02293-7 · Cited by 298
Evaluation of Text Generation: A Survey
Celikyilmaz, A., Clark, E., & Gao, J. (2020) · arXiv · doi:10.48550/arxiv.2006.14799 · Cited by 207
Good News, Everyone! Context Driven Entity-Aware Captioning for News Images
Biten, A. F., Gomez, L., Rusiñol, M., & Karatzas, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01275 · Cited by 136
Metadata
- Authors
- Bai, S., & An, S.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 311
- Pages
- 291-304
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2803259101
- Cited by
- 241
- References
- 22
- FWCI
- 10.22
- Type
- journal article
- Language
- en
- Open access
- closed
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