Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions

Ziyue Wang, Chi Chen, Peng Li, Yang Liu

Submission Type: Regular Long Paper

Submission Track: Question Answering

Submission Track 2: Language Grounding to Vision, Robotics and Beyond

Keywords: visual question answering, knowledge reasoning, in-context learning

TL;DR: Propose a framework where LLMs proactively ask questions for more image information for knowledge-based VQA, together with filtering models to refine the information.

Abstract:

Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge not only in natural language tasks, but also in some vision-language tasks such as open-domain knowledge-based visual question answering (OK-VQA). As images are invisible to LLMs, researchers convert images to text to engage LLMs into the visual question reasoning procedure. This leads to discrepancies between images and their textual representations presented to LLMs, which consequently impedes final reasoning performance.
To fill the information gap and better leverage the reasoning capability, we design a framework that enables LLMs to proactively ask relevant questions to unveil more details in the image, along with filters for refining the generated information. We validate our idea on OK-VQA and A-OKVQA. Our method continuously boosts the performance of baselines methods by an average gain of 2.15\% on OK-VQA, and achieves consistent improvements across different LLMs.

Submission Number: 2734