## 8 Limitations and Future Directions  
Despite the rapid development of image captioning, it is considered to be in the initial stages in Arabic. Some challenges in this field, such as accuracy, performance, and generalization to different domains and datasets, are still far from expected. In the following, we present several key paths for future directions in image captioning based on different aspects. * **Visual aspect. ** The visual aspect should be developed in choosing the visual model and the appropriate encoding method so that the model can accurately extract the image objects, their properties, and the relationships between them. Since this aspect of Arabic studies has not been sufficiently developed, most studies use CNN-based features without an attention mechanism. A good feature representation should be provided by extracting the objects and their relationships, using the attention mechanism to focus on the important regions in the image, and sending them to the language model to produce better descriptions. * **Language model aspect. ** Focusing on the transformer model with the Arabic language, it has proven its superiority in sequence modeling, and the reason for this is due to its ability to capture the relationships between each word and another in the sequence. Additionally, using pre-trained models saves time and resources, possibly leading to better results such as BERT [111]. It has several versions, such as mBERT [112], short for Multilingual BERT, which is pre-trained in several languages, including Arabic, AraBERT [113], and ArabicBERT [114]. \begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Ref**} & \multicolumn{6}{c}{**(CIDEr Score Optimization)**} \\ \cline{3-10}  & & **B-1** & **B-2** & **B-3** & **B-4** & **M** & **R** & **C** & **S** \\ \hline
2018 & RFNet [49] & 79.1 & 63.1 & 48.4 & 36.5 & 27.7 & 57.3 & 121.9 & 21.2 \\  & Bottom-Up [50] & 78.0 & - & - & 36.5 & 28.1 & 57.2 & 120.6 & 21.6 \\  & Up-Down [56] & 79.8 & & & 36.3 & 27.7 & 56.9 & 120.1 & 21.4 \\ \hline
2019 & AoANet [79] & 80.2 & - & - & 38.9 & 29.2 & 58.8 & 129.8 & 22.4 \\  & [PERSON] et al. [69] & 80.5 & - & - & 38.6 & 28.7 & 58.4 & 128.3 & 22.6 \\  & [PERSON] et al. [106] & 81.1 & 65.0 & 50.4 & 38.3 & 28.6 & 58.6 & 123.2 & 22.1 \\  & Obj-R+Rel-A+CIDEr [55] & 79.2 & 63.2 & 48.3 & 36.3 & 27.6 & - & 120.2 & - \\  & CAVP [7] & - & - & - & 38.6 & 28.3 & 58.5 & 126.3 & 21.6 \\  & MT [107] & 81.9 & - & - & 40.7 & 29.5 & 59.7 & 134.1 & - \\  & LBPF [77] & 80.5 & - & - & 38.3 & 28.5 & 58.4 & 127.6 & 22.0 \\  & ETA [70] & 81.5 & - & - & 39.3 & 28.8 & 58.9 & 126.6 & 22.7 \\ \hline
2020 & M2 Transformer [10] & 80.8 & - & - & 39.1 & 29.2 & 58.6 & 131.2 & 22.6 \\  & MAD+SAP (F) [6] & - & - & - & 38.6 & 28.7 & 58.5 & 128.8 & 22.2 \\  & NG-SAN [58] & - & - & - & 39.9 & 29.3 & 59.2 & 132.1 & 23.3 \\  & [PERSON] et al. [59] & 80.8 & - & - & 39.5 & 29.1 & 59.0 & 130.8 & 22.8 \\  & X-LAN [60] & 80.8 & 65.6 & 51.4 & 39.5 & 29.5 & 59.2 & 132.0 & 23.4 \\  & X-Transformer [60] & 80.9 & 65.8 & 51.5 & 39.7 & 29.5 & 59.1 & 132.8 & 23.4 \\ \hline
2021 & (w/ MAC) [72] & 81.5 & - & - & 39.5 & 29.3 & 58.9 & 131.6 & 22.8 \\  & DLCT [73] & 81.4 & - & - & 39.8 & 29.5 & 59.1 & 133.8 & 23.0 \\  & SATIC [82] & - & - & - & 38.4 & 28.8 & - & 129.0 & 22.7 \\  & DRT [71] & 81.7 & - & - & 40.4 & 29.5 & 59.3 & 133.2 & 23.3 \\  & SG2 Caps [67] & - & - & - & 33.0 & 26.2 & 55.6 & 112.3 & 19.4 \\  & AFT [80] & 80.5 & - & - & 38.7 & 29.2 & 58.4 & 130.1 & 22.5 \\ \hline
2022 & [PERSON] et al. [81] & 80.8 & - & - & 39.3 & 29.0 & 58.9 & 129.9 & - \\  & ArCo [62] & 82.8 & - & - & 41.4 & 30.4 & 60.4 & 139.7 & 24.5 \\ \hline
2023 & LATGeO [63] & 81.0 & - & - & 38.8 & 29.2 & 58.7 & 131.7 & 22.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The reported results from Microsoft COCO Karpathy test split [85], with CIDEr Score Optimization. * **Dataset aspect.