## 7 Discussion
\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
2015 & DMSM [53] & ✓ & & & - & - & - \\  & Mind’s Eye [52] & ✓ & & & RNN & 18.8 & - \\  & Hard-Attention [3] & & ✓ & & LSTM & 25.0 & - \\  & gLSTM [54] & ✓ & & & LSTM & 26.4 & 81.25 \\  & NIC [4] & ✓ & & & LSTM & 27.7 & 85.5 \\ \hline
2016 & ATT-FCN [47] & ✓ & & & LSTM & 30.4 & - \\ \hline
2017 & SCA-CNN [5] & & ✓ & & LSTM & 31.1 & - \\  & GLA [64] & ✓ & & ✓ & LSTM & 31.2 & 96.4 \\  & Adaptive Attention [76] & & ✓ & & LSTM & 33.2 & 108.5 \\ \hline
2018 & [PERSON] et al. [9] & ✓ & & & Transformer & - & - \\  & Up-Down [56] & & ✓ & & LSTM & 36.3 & 120.1 \\  & Bottom-Up [50] & & ✓ & ✓ & & Transformer & 36.5 & 120.6 \\  & RFNet [49] & & ✓ & & LSTM & 36.5 & 121.9 \\ \hline
2019 & Obj+R-Rel-A+CIDEr [55] & & & ✓ & LSTM & 36.3 & 120.2 \\  & [PERSON] et al. [106] & & ✓ & & LSTM & 38.3 & 123.2 \\  & CAVP [7] & & ✓ & & LSTM & 38.6 & 126.3 \\  & ETA [70] & & ✓ & & Transformer & 39.3 & 126.6 \\  & LBPF [77] & & ✓ & & LSTM & 38.3 & 127.6 \\  & [PERSON] et al. [69] & & ✓ & & Transformer & 38.6 & 128.3 \\  & AoANet [79] & & ✓ & & LSTM & 38.9 & 129.8 \\  & MT [107] & & ✓ & & Transformer & 40.7 & 134.1 \\ \hline
2020 & AICRL [51] & & ✓ & LSTM & - & - \\  & D-ada [48] & ✓ & & & LSTM & 32.6 & - \\  & MAD+SAP (F) [6] & & ✓ & LSTM & 38.6 & 128.8 \\  & [PERSON] et al. [59] & & ✓ & & Transformer & 39.5 & 130.8 \\  & M2 Transformer [10] & & ✓ & & Transformer & 39.1 & 131.2 \\  & X-LAN [60] & & ✓ & & LSTM & 39.5 & 132.0 \\  & NG-SAN [58] & & & & Transformer & 39.9 & 132.1 \\  & X-Transformer [60] & ✓ & & & Transformer & 39.7 & 132.8 \\ \hline
2021 & SG2 Caps [67] & & & ✓ & LSTM & 33.0 & 112.3 \\  & Trans[D2 GPO+MLE]+KG [65] & & & ✓ & Transformer & 34.39 & 112.60 \\  & SATIC [82] & & ✓ & & Transformer & 38.4 & 129.0 \\  & Dual- & & & ✓ & Transformer & 39.7 & 129.2 \\  & GCN+Transformer+CL [66] & & & & Transformer & 38.7 & 130.1 \\  & AFT [80] & & ✓ & & Transformer & 39.5 & 131.6 \\  & (w/ MAC) [72] & & ✓ & & Transformer & 40.4 & 133.2 \\  & DRT [71] & & ✓ & & Transformer & 39.8 & 133.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: An overview of models for image captioning based on deep learning. Scores are gathered from the corresponding papers. For the metrics, a higher value is better. **RQ2. Which deep learning techniques are used for caption generation? ** For many years, recurrent models have been used as standard sequential modeling, and their use has led to the creation of ingenious and effective concepts that may be incorporated into nonrecurrent systems. Nevertheless, they are difficult to train and have trouble maintaining long-term dependence. So, these limitations are mitigated by autoregressive and transformer-based systems that have lately gained favor. The transformer can take advantage of parallelism as it uses non-sequential processing. As shown in Table 8, the transformer has outperformed LSTM in many studies, such as MT [107] outperforming AoANet [79] in CIDEr score by 4.3 and in BLEU-4 score by 1.8. Many researchers have compared RNN and transformer using the same image feature extraction, such as [9; 60], and indicate that the transformer model outperforms the RNN model, X-LAN [60] (LSTM) and X-Transformer [60] (Transformer); they have plugged their X-Linear attention blocks into LSTM and Transformer-based models, as we can see in Table 8. X-Transformer outperforms X-LAN in CIDEr score by 0.8 and in BLEU-4 score by 0.2. **RQ3. Which evaluation mechanisms are used in the literature for image captioning? ** Measuring the model's performance concerning unobserved data is necessary after updating the model parameters to reduce the error between the predicted and ground truth values in the training stage. Evaluating a produced caption's quality is a challenging task complicated by the requirement that captions correctly relate to the input image and be grammatically correct and fluid.