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    <abstract>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</abstract>
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%0 Conference Proceedings
%T Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models
%A Jhalani, Manas
%A K M, Annervaz
%A Bhattacharyya, Pushpak
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F jhalani-etal-2024-precision
%X 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
%U https://aclanthology.org/2024.icon-1.3/
%P 21-36

Markdown (Informal)

[Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models](https://aclanthology.org/2024.icon-1.3/) (Jhalani et al., ICON 2024)

• Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models (Jhalani et al., ICON 2024)

ACL

• Manas Jhalani, Annervaz K M, and Pushpak Bhattacharyya. 2024. Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 21–36, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).