# How to find a good image-text embedding for remote sensing visual question answering?

Source: [How to find a good image-text embedding for remote sensing visual question answering?](<https://www.sota2.com/research/paper/68fa6add1e209131cea2ae26>)

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

Data updated: 2025-10-25T23:35:16.571Z

Authors: Christel Chappuis, Sylvain Lobry, Benjamin Kellenberger, Bertrand Le Saux, Devis Tuia

Published: 2021-09-24T09:48:28.000Z

## Abstract

Visual question answering (VQA) has recently been introduced to remote sensing to make information extraction from overhead imagery more accessible to everyone. VQA considers a question (in natural language, therefore easy to formulate) about an image and aims at providing an answer through a model based on computer vision and natural language processing methods. As such, a VQA model needs to jointly consider visual and textual features, which is frequently done through a fusion step. In this work, we study three different fusion methodologies in the context of VQA for remote sensing and analyse the gains in accuracy with respect to the model complexity. Our findings indicate that more complex fusion mechanisms yield an improved performance, yet that seeking a trade-of between model complexity and performance is worthwhile in practice.

## 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/2109.11848>)

[PDF](<https://arxiv.org/pdf/2109.11848.pdf>)