# Dual-Modal Transformer with Enhanced Inter- and Intra-Modality Interactions for Image Captioning
## 4 Results and Analysis
### Comparative Analysis
#### 4.3.3 Analysis using State-of-the-Art Methods  
The performance of the proposed dual-modal transformer is compared with several existing state-of-the-art methods on MS COCO dataset. In the review network [48], RNN-based decoders are used against CNN and RNN-based encoding units for caption prediction while in the adaptive model [49], the model toggles with the choice between attending to the detected objects from the image and choosing visual sentinel. The upcoming CNN architecture combined with the attention mechanism is employed in CNN + Attn. [50]. CPTR [51] exploits sequential raw images for making patches that are fed to both encoder and decoder in different forms. Simulating spatial relationships, the image transformer [35] employs improvised encoder and decoder units. A dual level collaborative transformer (DLCT) approach uses both grid and region features to generate image captions [45]. Researchers introduced the CAAG (context-aware auxiliary guidance) methodology to guide the captioning model, which learns whole semantics by reproducing the latest generation versus global contexts [34]. The encoder-decoder paradigm has been proposed whereas a global-enhanced encoder first encodes the original inputs into highly abstract localised representations and then extracts the intra- and inter-layer global representation. The decoder then implements the suggested global adaptive controller to iteratively incorporate the multimodal information while producing the caption word by word [52]. An Attentive Fourier-Augmented Image Captioning Transformer (AFT) based methodology has been proposed by the researchers. The main aim of this research is to develop a transformer-based image captioning system which can effectively utilise the information contained in both images and text, with an emphasis on image attributes to generate grammatically, semantically, and syntactically correct captions [53]. The performance of the proposed methodology has been compared and analysed with the state-of-the-art methods. The efficiency and efficacy of the proposed methodology have been compared and analysed using BLUE (B1, B2, B3, B4) score, METEOR [54] and ROUGE [36] values. The result shows that the dual modal transformer achieved the highest BLUE score of 85.01. The detailed comparative analysis has been depicted in Table 7:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Technique** & **B-1** & **B2** & **B-3** & **B-4** & **METEOR** & **ROUGE** \\ \hline Review Net [48] & 72.0 & 55.0 & 41.4 & 31.3 & 34.7 & 68.6 \\ \hline Adaptive [49] & 74.8 & 58.4 & 44.4 & 33.6 & 26.4 & 55.0 \\ \hline CNN + Attn [50] & 71.5 & 54.5 & 40.8 & 30.4 & 24.6 & 52.5 \\ \hline CPTR [51] & 81.7 & 66.6 & 52.2 & 40.0 & 29.1 & 59.2 \\ \hline Multi-modal transformer [5] & 81.7 & 66.8 & 52.4 & 40.4 & 29.4 & 59.6 \\ \hline Image Transformer [35] & 80.8 & - & - & 39.5 & 29.1 & 59.0 \\ \hline DLCT [42] & 82.4 & 67.4 & 52.8 & 40.6 & 29.8 & 59.8 \\ \hline CAAG [34] & 81.1 & 66.4 & 51.7 & 39.6 & 29.2 & 59.2 \\ \hline GET [54] & 82.1 & - & - & 40.6 & 29.8 & 59.6 \\ \hline AFCT [53] & 80.5 & - & - & 38.7 & 29.2 & 58.4 \\ \hline Dual-Modal Transformer (proposed) & 85.0 & 78.4 & 70.3 & 48.3 & 35.4 & 69.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: A comparison of the proposed method with related state-of-the-art techniques.