# Multimodal grid features and cell pointers for Scene Text Visual Question Answering

Source: [Multimodal grid features and cell pointers for Scene Text Visual Question Answering](<https://www.sota2.com/research/paper/68fa67c91e209131cea1862f>)

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

Data updated: 2025-10-25T22:22:05.587Z

Authors: Llu\\'is G\\'omez, Ali Furkan Biten, Rub\\\`en Tito, Andr\\'es Mafla, Mar\\c{c}al Rusi\\~nol, Ernest Valveny, Dimosthenis Karatzas

Published: 2020-06-01T13:17:44.000Z

## Abstract

This paper presents a new model for the task of scene text visual question answering, in which questions about a given image can only be answered by reading and understanding scene text that is present in it. The proposed model is based on an attention mechanism that attends to multi-modal features conditioned to the question, allowing it to reason jointly about the textual and visual modalities in the scene. The output weights of this attention module over the grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text the to the given question. Our experiments demonstrate competitive performance in two standard datasets. Furthermore, this paper provides a novel analysis of the ST-VQA dataset based on a human performance study.

## 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/2006.00923>)

[PDF](<https://arxiv.org/pdf/2006.00923.pdf>)