## 1 Introduction
### Previous Surveys  
Despite the impressive developments in image captioning in the last decade, automatic image captioning is still challenging. Some surveys have covered the techniques used in image captioning. We briefly give an overview of previous surveys in this section. The review paper [27] thoroughly analyzes deep learning methods used in image captioning until 2018. The survey's authors focused on reviewing deep learning models used for novel caption generation. The review covers various aspects, including learning types and architectures, feature mapping, and language models. Other techniques include novel object-based captioning, stylized captions, attention-based captioning, and semantic concept-based captioning. They also examine the datasets and evaluation metrics often utilized in deep-learning-based image captioning. [PERSON] et al. [28] review image captioning approaches comprehensively from 2015 to 2021. They focus on presenting different training strategies, comparing datasets, and evaluating metrics. Additionally, they quantitatively compare multiple state-of-the-art methods to determine the most significant technological advancements in training methods and architectures with discussions of the problems and challenges. [PERSON] et al. [29] review different image captioning methods in natural and medical domains from 2019 to 2022. They describe standard captioning techniques, either with or without reinforcement learning. By simulating the human processes of seeing, focusing, and telling, which are subsequently reflected in feature representation, visual encoding, and language development, respectively, they suggest a workflow for making image captions. Additionally, they present datasets, evaluation metrics, and loss functions with defining advantages and disadvantages of existing approaches. The paper [22] provides a brief overview of advancements in image captioning from 2016 to 2019 in a comprehensive systematic way. Different feature extractors have been investigated, such as GoogleNet (including all nine Inception models), AlexNet, ResNet, VGGNet, and DenseNet. Language models such as RNN, LSTM, CNN, GRU, and TPGN were also discussed. This comparison involves the evaluation metrics BLEU (1-4), CIDEr, and METEOR on MSCOCO and Flicker30k datasets. The paper by [PERSON] et al. [23] comprises a systematic literature review (SLR) from 2017 to 2019. They discuss the techniques and models used for deep-learning-based image captioning. They find that the most important models are CNNs, used to understand images. RNN and LSTM are used to generate language, and the most used evaluation metrics are BLEU and the databases MS COCO, Flicker30k, and Flicker8k, of which MS COCO is the most used. Additionally, it is discovered that LSTM and CNN have outperformed RNN and CNN. Encoder-decoder and attention mechanisms were discovered to be the two most effective methods. The work by [PERSON] et al. [30] focuses on recent deep-learning-based techniques between 2018 and 2020, such as adversarial learning, deep reinforcement, and attention mechanisms. They review a few techniques and implementation specifics, including the Conditional GAN-Based model, Meta-Learning, UpDown, OSCAR, and VIVO. They found that the Conditional GAN-Based model achieves the highest score. The survey by [PERSON] et al. [31] compares state-of-the-art techniques used in image captioning from 2011 to 2021, providing details of different architectures used, evaluation metrics, and available datasets. They divide the image captioning methods into categories to summarize them and discuss their advantages and limitations. Moreover, they quantitatively evaluate the relevant cutting-edge studies to identify current trends and prospects in image captioning. The paper by [PERSON] and [PERSON] [32] presents an overview of recent advances in encoder-decoder image captioning research. They determine that the main benefit of that architecture is that it can be trained end-to-end, directly mapping from images to sentences.