# Generating Question Relevant Captions to Aid Visual Question Answering

Source: [Generating Question Relevant Captions to Aid Visual Question Answering](<https://www.sota2.com/research/paper/68fa66e81e209131cea1311f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T03:08:54.506Z

Authors: Jialin Wu, Zeyuan Hu, Raymond J. Mooney

Published: 2019-06-03T00:42:08.000Z

## Abstract

Visual question answering (VQA) and image captioning require a shared body of general knowledge connecting language and vision. We present a novel approach to improve VQA performance that exploits this connection by jointly generating captions that are targeted to help answer a specific visual question. The model is trained using an existing caption dataset by automatically determining question-relevant captions using an online gradient-based method. Experimental results on the VQA v2 challenge demonstrates that our approach obtains state-of-the-art VQA performance (e.g. 68.4% on the Test-standard set using a single model) by simultaneously generating question-relevant captions.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Visual Question Answering on VQA v2 (test-std)](<https://www.sota2.com/research/sota/visual-question-answering-on-vqa-v2-test-std.md>): Visual Question Answering; VQA v2 (test-std); Accuracy: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1906.00513>)

[PDF](<https://arxiv.org/pdf/1906.00513.pdf>)