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Amir Atapour-Abarghouei

10 accepted papers

2026

EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover

Ghadah Alosaimi, Maha Alsayyari, Yixin Sun, Stamos Katsigiannis, Amir Atapour-Abarghouei, Toby Breckon
ICRA 2026poster

Brain–computer interfaces (BCIs) provide a hands-free control modality for mobile robotics, yet decoding user intent during real-world navigation remains challenging. This work presents a brain–robot control framework for offline decoding of driving commands during robotic rover operation. A 4WD Rov…

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2026

Exploring the Potentials of Spiking Neural Networks for Image Deraining

Shuang Chen, Tom Krajnik, Farshad Arvin, Amir Atapour-Abarghouei
AAAI 2026technical

Biologically plausible and energy-efficient frameworks such as Spiking Neural Networks (SNNs) have not been sufficiently explored in low-level vision tasks. Taking image deraining as an example, this study addresses the representation of the inherent high-pass characteristics of spiking neurons, spe

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2025

BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion

Sike Xiang, Shuang Chen, Amir Atapour-Abarghouei
EMNLP 2025

As multimodal large language models (MLLMs) advance, their large-scale architectures pose challenges for deployment in resource-constrained environments. In the age of large models, where energy efficiency, computational scalability and environmental sustainability are paramount, the development of

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2025

DEEP-SEA: Deep-Learning Enhancement for Environmental Perception in Submerged Aquatics

Shuang Chen, Ronald Thenius, Farshad Arvin, Amir Atapour-Abarghouei
IROS 2025

Continuous and reliable underwater monitoring is essential for assessing marine biodiversity, detecting ecological changes and supporting autonomous exploration in aquatic environments. Underwater monitoring platforms rely on mainly visual data for marine biodiversity analysis, ecological assessment

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2025

Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots

Shuang Chen, Yifeng He, Barry Lennox, Farshad Arvin, Amir Atapour-Abarghouei
ICRA 2025

Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cost, collaborative robots can greatly improve efficiency. These robots capture images from different positions, which must

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2025

Dur360BEV: A Real-World 360-Degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving

Wenke E, Chao Yuan, Li Li, Yixin Sun, Yona Falinie A. Gaus, Amir Atapour-Abarghouei +1
ICRA 2025

We present Dur360BEV, a novel spherical camera autonomous driving dataset equipped with a high-resolution 128-channel 3D LiDAR and a RTK-refined GNSS/INS system, along with a benchmark architecture designed to generate Bird-Eye-View (BEV) maps using only a single spherical camera. This dataset and b

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2024

Insights from the Use of Previously Unseen Neural Architecture Search Datasets

Rob Geada, David Towers, Matthew Forshaw, Amir Atapour-Abarghouei, A. Stephen McGough
CVPR 2024poster

The boundless possibility of neural networks which can be used to solve a problem - each with different performance - leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of removing the need for experts. Neural Architecture Se…

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2022

Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

Peter Bevan, Amir Atapour-Abarghouei
ICML 2022spotlight

Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma from skin lesion images, but prediction irregularities due to biases seen within the training data are an issue that should be addressed before widespread deployment is possible. In this…

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2019

Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach

Amir Atapour-Abarghouei, Toby P. Breckon
CVPR 2019poster

Robust geometric and semantic scene understanding is ever more important in many real-world applications such as autonomous driving and robotic navigation. In this paper, we propose a multi-task learning-based approach capable of jointly performing geometric and semantic scene understanding, namely…

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2018

Real-Time Monocular Depth Estimation Using Synthetic Data With Domain Adaptation via Image Style Transfer

Amir Atapour-Abarghouei, Toby P. Breckon
CVPR 2018poster

Monocular depth estimation using learning-based approaches has become promising in recent years. However, most monocular depth estimators either need to rely on large quantities of ground truth depth data, which is extremely expensive and difficult to obtain, or predict disparity as an intermediary…

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