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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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PMA-VQA: Progressive Multi-Scale Feature Fusion with Spatially Adaptive Attention for Remote Sensing Visual Question

Yifei He^1,2, Chen Qiu^1,2, Jinguang Gu^1,2

¹School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.

Sensors (Basel, Switzerland)

|May 4, 2026

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This study introduces PMA-VQA, a novel approach for remote sensing visual question answering (RS-VQA) that improves geospatial reasoning in aerial images. PMA-VQA enhances accuracy and robustness by progressively fusing multi-scale features with spatially adaptive attention.

Area of Science:

Earth Observation
Computer Vision
Artificial Intelligence

Background:

Remote sensing visual question answering (RS-VQA) is crucial for intelligent Earth observation, enabling interactive analysis of high-resolution aerial imagery.
Existing RS-VQA models often struggle with geospatial reasoning due to the inherent multi-scale object variance and spatial heterogeneity of remote sensing scenes.
Current models tend to over-rely on linguistic priors rather than grounding answers in visual evidence.

Purpose of the Study:

To address the limitations in fine-detail geospatial reasoning for RS-VQA.
To develop a model that effectively integrates hierarchical, multi-level, language-informed features for improved visual evidence-based reasoning.
To enhance the accuracy and robustness of RS-VQA systems when analyzing complex aerial imagery.

Keywords:

progressive multi-scale fusion remote sensing visual question answering spatially adaptive attention

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Main Methods:

Proposed PMA-VQA (progressive multi-scale feature fusion with spatially adaptive attention) for hierarchical feature integration.
Introduced Spatial Attention Aggregation Module (SAAM) for aligning cross-modal features with semantic context using adaptive gating.
Developed Progressive Feature Fusion and Classification Module (PFCM) to integrate multi-scale representations from semantic abstractions and spatial details.

Main Results:

PMA-VQA demonstrated superior performance over existing methods on RS-VQA LR and HR benchmarks, achieving higher accuracy and robustness.
Evaluations on the HRVQA benchmark confirmed the effectiveness of the SAAM and PFCM across diverse remote sensing scenes.
The proposed method shows significant improvements in visual evidence-based reasoning for complex geospatial queries.

Conclusions:

PMA-VQA effectively tackles the challenges of multi-scale variance and spatial heterogeneity in remote sensing scenes.
The integration of SAAM and PFCM enables more accurate and robust geospatial reasoning in RS-VQA.
The developed approach advances the field of intelligent Earth observation by enhancing interactive image querying capabilities.