## 7 Discussion
A human judgment is still the most accurate way to evaluate how well a caption suits an image. Nevertheless, human evaluation is costly and unrepeatable, and it is impossible to compare various strategies fairly. Existing automated evaluation techniques frequently evaluate the produced captions' quality by contrasting them with human reference captions, as described in Section 5. Table 9 shows the results of the experiments on the MS COCO evaluation server that contains 40 captions for 5000 randomly selected images from the MS COCO testing dataset. The evaluation server guarantees consistency in evaluating automatic caption generation methods. **RQ4. Which datasets are used for image captioning? ** The dataset is one of the key elements of image captioning, which is used to develop and evaluate the model. The several datasets that are available online are detailed in Section 4. The following requirements should be taken into account when selecting the dataset:  
* **Size. ** Image captioning is a complex problem. It is a combination of two tasks requiring knowledge of handling images and text, so it requires a large amount of data for the model to generate accurate captions. * **Data quality. ** Having many data with low quality has a side effect on performance. The data must be of high quality so that the model can accurately detect the objects in the images. * **Diversity. ** To make the model generalizable to new domains, tasks, and scenarios. In particular, if they differ considerably from the training data regarding content, style, or context, the models developed using current datasets may need to perform better on unseen images or texts. For instance, a model trained on MSCOCO could not caption fashion or medical images. * **Annotation quality. ** Annotation of images in any dataset must be consistent, complete, accurate, and contain no spelling or grammatical errors. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Year** & **Model** & **Global** & **Grid** & **Region** & **Graph** & **Language Model** & **BLEU-4** & **CIDEr** \\ \hline
2022 & Dual-Modal former [61] & Trans- & ✓ & ✓ & Transformer & - & - \\  & [PERSON] et al. [81] & & & ✓ & & Transformer & 39.3 & 129.9 \\  & ReFormer [68] & & & ✓ & Transformer & 39.8 & 131.9 \\  & ArCo [62] & & & ✓ & & Transformer & 41.4 & 139.7 \\ \hline
2023 & LATGeO [63] & & ✓ & & Transformer & 38.8 & 131.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._* **Linguistic richness. ** The captions for the images should be more accurate, sizable, and varied datasets that can accurately represent the diversity and richness of linguistic and visual information in the actual world. * **Complexity. ** To use the image captioning task for diverse applications, the dataset must contain various objects, attributes, and interactions. This means the dataset should have various objects, such as man, dog, ball, and tree; different interactions, such as standing, walking, running, and extending; and more complex interactions, such as flipping and kicking. Most of the data in these datasets are high-resolution and contain different postural activities such as running, walking, talking, and kicking. However, the captions for the images are basic, inaccurate, or repetitions, which may not match viewers' expectations or natural language usage. **RQ5: Which loss functions are used to train image captioning models? ** Most studies follow a two-phase sequence training procedure, including pre-training through supervised learning and fine-tuning through reinforcement learning. (The traditional studies follow just the first phase as shown in Table 10). The cross-entropy loss is optimized for pre-training between the ground truth and the probability distribution produced. Sequential models for text are often trained to use back-propagation to maximize the likelihood of the following ground-truth word given the preceding ground-truth word, and this is called \"Teacher-Forcing\" [108].