A survey on image captioning methods

Image captioning is a task that Involves Natural Language Processing concepts to recognize the context of an image and describe them in a natural language like English. It requires good knowledge of Deep learning. Python, working on Jupyter notebooks, Keras library, Numpy, and Natural language processing It is a Python based project where we will use deep learning techniques of Convolutional Neural Networks and a type of Recurrent Neural Network (LSTM) together. The biggest challenge is most definitely being able to create a description that must capture not only the objects contained in an image, but also express how these objects relate to each other. Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing here, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. It could have great impact, for instance by helping visually impaired people better understand the content of images on the web.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-31
DOI
https://doi.org/10.5281/zenodo.17339915
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

A survey on image captioning methods

Zenodo (CERN European Organization for Nuclear Research)
Multimodal Machine Learning Applications
article

A survey on image captioning methods

article en

Abstract

Image captioning is a task that Involves Natural Language Processing concepts to recognize the context of an image and describe them in a natural language like English. It requires good knowledge of Deep learning. Python, working on Jupyter notebooks, Keras library, Numpy, and Natural language processing It is a Python based project where we will use deep learning techniques of Convolutional Neural Networks and a type of Recurrent Neural Network (LSTM) together. The biggest challenge is most definitely being able to create a description that must capture not only the objects contained in an image, but also express how these objects relate to each other. Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing here, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. It could have great impact, for instance by helping visually impaired people better understand the content of images on the web.

Zenodo (CERN European Organization for Nuclear Research)
English and Foreign Languages University (IN)
Quality Education
Openalex Percentile: Top 44%
Multimodal Machine Learning Applications
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A survey on image captioning methods · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS