## 6 Conclusions and Future Work  
In this research, we proposed the Dynamic Context-Aware Transformer (DCAT), a novel image captioning model tailored for the Tamil language. DCAT distinguishes itself by integrating a Vision Transformer (ViT) as the encoder and a Generative Pre-trained Transformer (GPT-3) as the decoder, bridged effectively by a specialized Context Embedding Layer. This architecture is a significant innovation in addressing the challenges of image captioning in low-resource languages, particularly in handling the intricacies of Tamil. Our experimental results validate the efficacy of DCAT. The model achieves superior performance with a BLEU score of 0.7425, METEOR score of 0.4391, Composite Evaluation Score (CES) of 0.6279, and Contextual Accuracy (CA) of 0.4182 on the Flickr8k dataset. It consistently outperforms existing models in these metrics across diverse datasets, including Flickr30k and MSCOCO. The model's ability to dynamically model context, both spatially and temporally, underlines its innovative approach. Despite its strengths, DCAT faces limitations in computational intensity and scalability. The model's complexity demands significant computational resources, posing challenges in terms of efficiency and broader applicability.  
Looking ahead, our future work will focus on addressing these limitations. We aim to reduce the computational demands of DCAT without sacrificing its performance. Strategies under consideration include model pruning, quantization, and exploring more efficient architectural designs. Additionally, we plan to extend the model's capabilities to other languages and multimodal tasks. Applications in video captioning and question-answering represent exciting frontiers for DCAT's technology. Further research will also tackle the challenges of data biases and model interpretability, ensuring that DCAT not only performs efficiently but also remains fair and understandable in its operations. Through these endeavors, we anticipate making DCAT a more versatile and accessible tool for natural language processing and computer vision tasks, paving the way for broader applications in AI-driven content generation and analysis.  
Data curation, J.P.V. and G.S.; Formal analysis, J.P.V., M.M. and M.R.; Funding acquisition, P.W.; Investigation, J.P.V.; Methodology, J.P.V. and G.S.; Project administration, M.R. and P.W.; Resources, A.A.V.S., M.M. and M.R.; Software, A.A.V.S.; Supervision, M.R. and P.W.; Validation, A.A.V.S.; Visualization, M.M. and P.W.; Writing\(-\)original draft, J.P.V. and A.A.V.S.; Writing\(-\)review and editing, G.S., M.R. and P.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors appreciate the support provided by the National Research and Development Agency (ANID) through the FONDECYT Regular grant number 1220556. Additional funding was provided by the Research Project PINV01-743 of the National Council of Science and Technology (CONACYT). Furthermore, the authors acknowledge the support from the British Council under the IND/CONT/C/23-24/14 - Going Global Partnerships Industry-Academia Collaborative Grant.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Acknowledgments:** We sincerely thank Karpagam College of Engineering, Coimbatore, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, and VIT-AP University, Amaravathi for their support, resources, and encouragement during this research. The authors also thank the Machines and Control Research Institute (PEMC), University of Nottingham, UK, for their valuable guidance and collaboration.  
**Conflicts of Interest:** The authors declare no conflicts of interest.