# Fully Authentic Visual Question Answering Dataset from Online Communities

Source: [Fully Authentic Visual Question Answering Dataset from Online Communities](<https://www.sota2.com/research/paper/68fa74211e209131cea5fd3c>)

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

Data updated: 2025-10-26T03:02:53.994Z

Authors: Chongyan Chen, Mengchen Liu, Noel Codella, Yunsheng Li, Lu Yuan, Danna Gurari

Published: 2023-11-27T06:19:00.000Z

## Abstract

Visual Question Answering (VQA) entails answering questions about images. We introduce the first VQA dataset in which all contents originate from an authentic use case. Sourced from online question answering community forums, we call it VQAonline. We characterize this dataset and how it relates to eight mainstream VQA datasets. Observing that answers in our dataset tend to be much longer (i.e., a mean of 173 words) and so incompatible with standard VQA evaluation metrics, we instead utilize popular metrics for longer text evaluation for evaluating six state-of-the-art VQA models on VQAonline and report where they struggle most. Finally, we analyze which evaluation metrics align best with human judgments. To facilitate future extensions, we publicly-share the dataset at: https://vqaonline.github.io/.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2311.15562>)

[PDF](<https://arxiv.org/pdf/2311.15562.pdf>)