mlsanityData to 7 Oct 2026

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

arXiv 2109.11848 ↗First posted 24 Sep 2021cs.CVcited by 6

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 24 Sep 2021.

Builds on

21 of 29 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2015VQA: Visual Question Answeringcited by 2,489
  4. 2015Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Bookscited by 1,041
  5. 2015Neural Module Networkscited by 375
  6. 2015Skip-Thought Vectorscited by 321
  7. 2015Stacked Attention Networks for Image Question Answeringcited by 273
  8. 2016Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Groundingcited by 243
  9. 2019BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understandingcited by 231
  10. 2020RSVQA: Visual Question Answering for Remote Sensing Datacited by 197
  11. 2020FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understandingcited by 181
  12. 2017MUTAN: Multimodal Tucker Fusion for Visual Question Answeringcited by 137
9 more
  1. 2017Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answeringcited by 117
  2. 2019Multimodal Intelligence: Representation Learning, Information Fusion, and Applicationscited by 89
  3. 2015Explicit Knowledge-based Reasoning for Visual Question Answeringcited by 85
  4. 2016Learning to Compose Neural Networks for Question Answeringcited by 85
  5. 2017Beyond Bilinear: Generalized Multimodal Factorized High-order Pooling for Visual Question Answeringcited by 79
  6. 2019BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detectioncited by 62
  7. 2015Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sourcescited by 42
  8. 2021Towards a Collective Agenda on AI for Earth Science Data Analysiscited by 27
  9. 2015ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answeringcited by 23

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 0; 2025: 1.

6 papers.

  1. 2021Change Detection Meets Visual Question Answeringcited by 29
  2. 2023Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applicationscited by 27
  3. 2022Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensingcited by 13
  4. 2023Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Imagescited by 7
  5. 2023The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluationcited by 4
  6. 2025SAR Strikes Back: A New Hope for RSVQAcited by 3