English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Visual question answering (VQA) methods in remote sensing (RS) aim to answernatural language questions with respect to an RS image. Most of the existingmethods require a large amount of computational resources, which limits theirapplication in operational scenarios in RS. To address this issue, in thispaper we present an effective lightweight transformer-based VQA in RS(LiT-4-RSVQA) architecture for efficient and accurate VQA in RS. Ourarchitecture consists of: i) a lightweight text encoder module; ii) alightweight image encoder module; iii) a fusion module; and iv) aclassification module. The experimental results obtained on a VQA benchmarkdataset demonstrate that our proposed LiT-4-RSVQA architecture providesaccurate VQA results while significantly reducing the computationalrequirements on the executing hardware. Our code is publicly available athttps://git.tu-berlin.de/rsim/lit4rsvqa.

LiT-4-RSVQA: Lightweight Transformer-based Visual Question Answering in Remote Sensing