# HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images

arXiv: https://arxiv.org/abs/2301.09460 · HTML: https://mlsanity.com/paper/2301.09460/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 58 references that are in the map.
- First posted on arXiv 23 Jan 2023.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Stand-Alone Self-Attention in Vision Models](https://mlsanity.com/paper/1906.05909/) (2019)
- [Unified Vision-Language Pre-Training for Image Captioning and VQA](https://mlsanity.com/paper/1909.11059/) (2019)
- [CLEVRER: CoLlision Events for Video REpresentation and Reasoning](https://mlsanity.com/paper/1910.01442/) (2019)
- [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://mlsanity.com/paper/2103.10697/) (2021)
- [Deep Modular Co-Attention Networks for Visual Question Answering](https://mlsanity.com/paper/1906.10770/) (2019)
- [Skip-Thought Vectors](https://mlsanity.com/paper/1506.06726/) (2015)
- [Bilinear Attention Networks](https://mlsanity.com/paper/1805.07932/) (2018)
- [Stacked Attention Networks for Image Question Answering](https://mlsanity.com/paper/1511.02274/) (2015)
- [Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding](https://mlsanity.com/paper/1606.01847/) (2016)
- [ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph](https://mlsanity.com/paper/2006.16934/) (2020)
- [Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges](https://mlsanity.com/paper/2102.12219/) (2021)
- [RSVQA: Visual Question Answering for Remote Sensing Data](https://mlsanity.com/paper/2003.07333/) (2020)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [Adaptive Attention Span in Transformers](https://mlsanity.com/paper/1905.07799/) (2019)
- [MUTAN: Multimodal Tucker Fusion for Visual Question Answering](https://mlsanity.com/paper/1705.06676/) (2017)

## Built on by (most cited first, 21 of 21)

- [Zoom-Refine: Boosting High-Resolution Multimodal Understanding via Localized Zoom and Self-Refinement](https://mlsanity.com/paper/2506.01663/) (2025)
- [Look Where It Matters: Training-Free Ultra-HR Remote Sensing VQA via Adaptive Zoom Search](https://mlsanity.com/paper/2511.20460/) (2025)
- [A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs](https://mlsanity.com/paper/2512.17319/) (2025)
- [Asking like Socrates: Socrates helps VLMs understand remote sensing images](https://mlsanity.com/paper/2511.22396/) (2025)
- [VLRS-Bench: A Vision-Language Reasoning Benchmark for Remote Sensing](https://mlsanity.com/paper/2602.07045/) (2026)
- [Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering](https://mlsanity.com/paper/2411.15770/) (2024)
- [EarthVL: A Progressive Earth Vision-Language Understanding and Generation Framework](https://mlsanity.com/paper/2601.02783/) (2026)
- [HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding?](https://mlsanity.com/paper/2504.18406/) (2025)
- [Multimodal Rationales for Explainable Visual Question Answering](https://mlsanity.com/paper/2402.03896/) (2024)
- [Geo3DVQA: Evaluating Vision-Language Models for 3D Geospatial Reasoning from Aerial Imagery](https://mlsanity.com/paper/2512.07276/) (2025)
- [Reasoning with Pixel-level Precision: QVLM Architecture and SQuID Dataset for Quantitative Geospatial Analytics](https://mlsanity.com/paper/2601.13401/) (2026)
- [Revisiting Change VQA in Remote Sensing with Structured and Native Multimodal Qwen Models](https://mlsanity.com/paper/2604.18429/) (2026)
- [FlameVQA: A Physically-Grounded UAV Wildfire VQA Benchmark with Radiometric Thermal Supervision](https://mlsanity.com/paper/2606.27128/) (2026)
- [CosFly-VLA: A Spatially Aware Vision-Language-Action Model for UAV Tracking](https://mlsanity.com/paper/2607.15004/) (2026)
- [ReVA: A Scene-Centric Dataset Beyond Repetition for Remote Sensing Video Question Answering](https://mlsanity.com/paper/2609.35507/) (2026)
- [WildFireVQA: A Large-Scale Radiometric Thermal VQA Benchmark for Aerial Wildfire Monitoring](https://mlsanity.com/paper/2604.20190/) (2026)
- [Beyond Appearance: Can Multimodal Large Language Models Exploit Vertical Structure for Remote Sensing Natural Scene Understanding?](https://mlsanity.com/paper/2605.25784/) (2026)
- [ForestHG-Trace: Traceable Long-Horizon Ecological Reasoning over Large-Scale Forest Scenes](https://mlsanity.com/paper/2605.27590/) (2026)
- [GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence](https://mlsanity.com/paper/2606.17246/) (2026)
- [Semantic-Driven Scale and Spatial Selection for Efficient Cross-Modal Alignment in Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2606.30244/) (2026)
- [Harnessing Domain Specialists in Multimodal Mixture-of-Experts for Efficient Adaptation](https://mlsanity.com/paper/2610.02123/) (2026)

## Abstract (arXiv)

Visual question answering (VQA) is an important and challenging multimodal task in computer vision. Recently, a few efforts have been made to bring VQA task to aerial images, due to its potential real-world applications in disaster monitoring, urban planning, and digital earth product generation. However, not only the huge variation in the appearance, scale and orientation of the concepts in aerial images, but also the scarcity of the well-annotated datasets restricts the development of VQA in this domain. In this paper, we introduce a new dataset, HRVQA, which provides collected 53512 aerial images of 1024*1024 pixels and semi-automatically generated 1070240 QA pairs. To benchmark the understanding capability of VQA models for aerial images, we evaluate the relevant methods on HRVQA. Moreover, we propose a novel model, GFTransformer, with gated attention modules and a mutual fusion module. The experiments show that the proposed dataset is quite challenging, especially the specific attribute related questions. Our method achieves superior performance in comparison to the previous state-of-the-art approaches. The dataset and the source code will be released at https://hrvqa.nl/.