Progress in Combining Image Captioning and Visual Question Answering Tasks

The combination of image captioning and visual question answering (VQA) tasks has emerged as a captivating and innovative research domain. The goal of the image captioning task is generating a textual description that succinctly describes the contents of an image, while VQA focuses on providing natural language responses to questions posed about the image. Both tasks necessitate a profound comprehension of the image’s content, as well as the semantic relationships within it, while also requiring the ability to generate responses in a natural language format. The rapid progress in deep learning has propelled significant advancements in both these tasks. In this paper, we present a comprehensive overview of recent advancements in integrating image captioning and VQA tasks. Initially, we delve into the intricacies of image captioning and VQA tasks individually, followed by an exploration of various methodologies for their integration. Additionally, we examine the associated challenges and propose strategies to overcome them. Moreover, we analyze the datasets and evaluation metrics commonly employed in these tasks. The paper concludes by highlighting the importance of generating context-based captions that can effectively answer the most probable questions related to the image, thereby facilitating the VQA task. Overall, this review highlights the notable progress achieved in combining image captioning and VQA, while acknowledging the persisting challenges and outlining opportunities for future research in this dynamic and evolving field. Importantly, the integration of these tasks holds immense potential for enhancing the performance of real-world applications such as robotics, autonomous vehicles and image search.