Papers by Pulkit Agarwal
IndiFoodVQA: Advancing Visual Question Answering and Reasoning with a Knowledge-Infused Synthetic Data Generation Pipeline (2024.findings-eacl)
Copied to clipboard
| Challenge: | Large Vision Language Models lack domain-specific data for reasoning on complex problems. |
| Approach: | They propose to use explicit knowledge-infused questions, answers, and reasons to answer and reason upon the questions. |
| Outcome: | The proposed model improves by 25% over the baseline model. |
Finding Needles in Images: Can Multi-modal LLMs Locate Fine Details? (2025.acl-long)
Copied to clipboard
| Challenge: | Recent advances in Multi-modal Large Language Models (MLLMs) have fundamentally transformed how machines understand and reason about visual information. |
| Approach: | They propose a benchmark to evaluate MLLMs' ability to locate and reason about fine-grained details within complex documents including newspapers, menus, and lecture images. |
| Outcome: | The proposed method improves on existing methods and shows that it can handle fine-grained document understanding tasks. |
RG-VQA: Leveraging Retriever-Generator Pipelines for Knowledge Intensive Visual Question Answering (2025.findings-emnlp)
Copied to clipboard
Settaluri Lakshmi Sravanthi, Pulkit Agarwal, Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda, Ankit Mishra, Kiran Pradeep, Srihari K B, Godawari Sudhakar Rao, Pushpak Bhattacharyya
| Challenge: | Existing methods to improve the reasoning capabilities of VQA systems are limited due to complexity of graph neural networks and end-to-end training. |
| Approach: | They propose a method to integrate Dense Passage Retrievers with Vision Language Models to boost the reasoning capabilities of VQA systems. |
| Outcome: | The proposed method outperforms human accuracy and GPT-4 in the ScienceQA dataset. |