Visual Question Answering in Malayalam Text

One of the most important tasks in combining computer vision and natural language understanding is Visual Question Answering (VQA). This paper focuses on the VQA problem in Malayalam, a language known for its unique Dravidian linguistic characteristics. The robust LXMERT model, a bidirectional encoder-decoder architecture capable of handling multimodal input, is used to achieve this goal. Extensive testing on an independent set of Malayalam images and questions highlights the feasibility of building a trustworthy VQA model on a Malayalam dataset. The method shows adaptability when answering questions about Malayalam images, which creates opportunities for image retrieval, annotation, and possibly image-based machine translation. This demonstrates how the model can be applied to real-world situations in a variety of fields.