Summary of Dlava: Document Language and Vision Assistant For Answer Localization with Enhanced Interpretability and Trustworthiness, by Ahmad Mohammadshirazi et al.
DLaVA: Document Language and Vision Assistant for Answer Localization with Enhanced Interpretability and Trustworthiness
by Ahmad Mohammadshirazi, Pinaki Prasad Guha Neogi, Ser-Nam Lim, Rajiv Ramnath
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel method called DLaVA for Visual Question Answering (VQA) on document images. Existing approaches often lack interpretability and fail to precisely localize answers within the document, making it difficult for users to verify responses and understand the reasoning process. To address this, DLaVA enhances Multimodal Large Language Models (MLLMs) with answer localization capabilities, enabling users to trace the model’s reasoning. The approach integrates image annotation directly into the MLLM pipeline, improving interpretability by grounding responses in spatially annotated visual content. The paper presents both OCR-dependent and OCR-free architectures, with the OCR-free approach eliminating the need for separate text recognition components. Experimental results on standard datasets demonstrate that DLaVA achieves state-of-the-art (SOTA) performance, significantly enhancing model transparency and reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to ask a computer questions about pictures in books or documents. This paper is about making computers better at understanding what’s going on in these images. Right now, computers are good at answering simple questions, but they often don’t explain how they got the answer. The authors of this paper want to change that by creating a system that can not only answer questions but also show you where the answer comes from within the image. They did this by combining two different types of computer programs: one that’s good at understanding text and another that’s good at understanding images. This combination allows the computer to give more accurate answers and explain how it got them. The authors tested their system on some standard datasets and found that it works really well. |
Keywords
» Artificial intelligence » Grounding » Question answering
