114 citations · 579 across the 79 of their papers we have counts for
85 papers
VeriDrive: Verifiable Counterfactual Supervision for Cost-Efficient Vision-Language Planning
Zikai Zhang, Hubert P. H. Shum, Toby P. Breckon
Vision-language driving models increasingly use reasoning supervision to bridge perception, prediction, and planning, but existing driving rationales are often free-form and expens…
Mind2Drive: Predicting Driver Intentions from EEG in Real-world On-Road Driving
Ghadah Alosaimi, Hanadi Alhamdan, Wenke E +3
Predicting driver intention from neurophysiological signals offers a promising pathway for enhancing proactive safety in advanced driver assistance systems, yet remains challenging…
EEG-Driven Intention Decoding: Offline Deep Learning Benchmarking on a Robotic Rover
Ghadah Alosaimi, Maha Alsayyari, Yixin Sun +3
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 pre…
KD360-VoxelBEV: LiDAR and 360-degree Camera Cross Modality Knowledge Distillation for Bird's-Eye-View Segmentation
Wenke E, Yixin Sun, Jiaxu Liu +3
We present the first cross-modality distillation framework specifically tailored for single-panoramic-camera Bird's-Eye-View (BEV) segmentation. Our approach leverages a novel LiDA…
TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions
Yixin Sun, Li Li, Wenke E +2
Detecting traversable pathways in unstructured outdoor environments remains a significant challenge for autonomous robots, especially in critical applications such as wide-area sea…
DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification
Matt Poyser, Toby P. Breckon
In order to address the scalability challenge within Neural Architecture Search (NAS), we speed up NAS training via dynamic hard example mining within a curriculum learning framewo…
Dream-Box: Object-wise Outlier Generation for Out-of-Distribution Detection
Brian K. S. Isaac-Medina, Toby P. Breckon
Deep neural networks have demonstrated great generalization capabilities for tasks whose training and test sets are drawn from the same distribution. Nevertheless, out-of-distribut…
TFDM: Time-Variant Frequency-Based Point Cloud Diffusion with Mamba
Jiaxu Liu, Li Li, Hubert P. H. Shum +1
Diffusion models currently demonstrate impressive performance over various generative tasks. Recent work on image diffusion highlights the strong capabilities of Mamba (state space…
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 +4
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 bench…
FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection
Brian K. S. Isaac-Medina, Mauricio Che, Yona F. A. Gaus +2
Modern machine learning models, that excel on computer vision tasks such as classification and object detection, are often overconfident in their predictions for Out-of-Distributio…
TraIL-Det: Transformation-Invariant Local Feature Networks for 3D LiDAR Object Detection with Unsupervised Pre-Training
Li Li, Tanqiu Qiao, Hubert P. H. Shum +1
3D point clouds are essential for perceiving outdoor scenes, especially within the realm of autonomous driving. Recent advances in 3D LiDAR Object Detection focus primarily on the…
Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier Synthesis
Brian K. S. Isaac-Medina, Yona Falinie A. Gaus, Neelanjan Bhowmik +1
Object detection is a pivotal task in computer vision that has received significant attention in previous years. Nonetheless, the capability of a detector to localise objects out o…
RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
Li Li, Hubert P. H. Shum, Toby P. Breckon
3D point clouds play a pivotal role in outdoor scene perception, especially in the context of autonomous driving. Recent advancements in 3D LiDAR segmentation often focus intensely…
DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications
Li Li, Khalid N. Ismail, Hubert P. H. Shum +1
We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth esti…
Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery
Yona Falinie A. Gaus, Neelanjan Bhowmik, Brian K. S. Isaac-Medina +1
The Segment Anything Model (SAM) is a deep neural network foundational model designed to perform instance segmentation which has gained significant popularity given its zero-shot s…
Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics
Seyma Yucer, Amir Atapour Abarghouei, Noura Al Moubayed +1
Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement…
U3DS: Unsupervised 3D Semantic Scene Segmentation
Jiaxu Liu, Zhengdi Yu, Toby P. Breckon +1
Contemporary point cloud segmentation approaches largely rely on richly annotated 3D training data. However, it is both time-consuming and challenging to obtain consistently accura…
On Pixel-level Performance Assessment in Anomaly Detection
Mehdi Rafiei, Toby P. Breckon, Alexandros Iosifidis
Anomaly detection methods have demonstrated remarkable success across various applications. However, assessing their performance, particularly at the pixel-level, presents a comple…
Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers
Abril Corona-Figueroa, Sam Bond-Taylor, Neelanjan Bhowmik +4
Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment. For…
Racial Bias within Face Recognition: A Survey
Seyma Yucer, Furkan Tektas, Noura Al Moubayed +1
Facial recognition is one of the most academically studied and industrially developed areas within computer vision where we readily find associated applications deployed globally.…
Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation
Li Li, Hubert P. H. Shum, Toby P. Breckon
Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervis…
ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction
Zhengdi Yu, Shaoli Huang, Chen Fang +2
Reconstructing two hands from monocular RGB images is challenging due to frequent occlusion and mutual confusion. Existing methods mainly learn an entangled representation to encod…
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption
Jack W Barker, Neelanjan Bhowmik, Yona Falinie A Gaus +1
Anomaly detection is the task of recognising novel samples which deviate significantly from pre-establishednormality. Abnormal classes are not present during training meaning that…
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
Benjamin Kiefer, Matej Kristan, Janez Perš +70
The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and…