Manas Jhalani

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2024

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Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models
Manas Jhalani | Annervaz K M | Pushpak Bhattacharyya
Proceedings of the 21st International Conference on Natural Language Processing (ICON)

In the realm of multimodal tasks, Visual Question Answering (VQA) plays a crucial role by addressing natural language questions grounded in visual content. Knowledge-Based Visual Question Answering (KBVQA) advances this concept by adding external knowledge along with images to respond to questions. We introduce an approach for KBVQA, augmenting the existing vision-language transformer encoder-decoder (OFA) model . Our main contribution involves enhancing questions by incorporating relevant external knowledge extracted from knowledge graphs, using a dynamic triple extraction
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