LIT-4-RSVQA: Lightweight Transformer-Based Visual Question Answering in Remote Sensing-Reference-Cited by-同舟云学术

LIT-4-RSVQA: Lightweight Transformer-Based Visual Question Answering in Remote Sensing

Published:2023-07-16 Issue: Volume: Page:
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Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Hackel Leonard¹,Clasen Kai Norman¹,Ravanbakhsh Mahdyar²,Demir Begüm¹

Affiliation:

1. Technische Universität Berlin,Germany

2. Zalando SE,Berlin,Germany

Funder

European Research Council

European Space Agency

Publisher

IEEE

Link

Reference17 articles.

1. An image is worth 16x16 words: Transformers for image recognition at scale;dosovitskiy,2020

2. Attention is all you need;vaswani;NeurIPS,2017

3. RSVQA meets BigEarth-Net: A new, large-scale, visual question answering dataset for remote sensing;lobry;IEEE IGARSS,2021

4. Well-read students learn better: On the importance of pre-training compact models;turc,2019

5. XCiT: Cross-covariance image transformers;el-nouby;NeurIPS,2021

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