114 citations · 579 across the 79 of their papers we have counts for
7 papers · 1 filter
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…