# VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning

Source: [VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning](<https://www.sota2.com/research/paper/68fa6eb51e209131cea41a5c>)

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

Data updated: 2025-10-26T01:04:50.062Z

Authors: Kang Chen, Xiangqian Wu

Published: 2023-03-05T10:32:26.000Z

## Abstract

The ideal form of Visual Question Answering requires understanding, grounding and reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. However, most existing VQA benchmarks are limited to just picking the answer from a pre-defined set of options and lack attention to text. We present a new challenge with a dataset that contains 23,781 questions based on 10124 image-text pairs. Specifically, the task requires the model to align multimedia representations of the same entity to implement multi-hop reasoning between image and text and finally use natural language to answer the question. The aim of this challenge is to develop and benchmark models that are capable of multimedia entity alignment, multi-step reasoning and open-ended answer generation.

## 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/2303.02635>)

[PDF](<https://arxiv.org/pdf/2303.02635.pdf>)