1 code implementation • 1 Mar 2023 • Kaiyu Guo, Brian Lovell
Unlike previous methods focusing on distribution alignment, our algorithm is designed to disperse domain information in the embedding space.
Ranked #3 on Domain Generalization on PACS
no code implementations • 20 Dec 2022 • Meng Li, Chaoyi Li, Can Peng, Brian Lovell
Extensive experiments on the histopathology datasets show that leveraging our synthetic augmentation framework results in significant and consistent improvements in classification performance.
no code implementations • 20 Dec 2022 • Meng Li, Brian Lovell
The teacher learns to generate curriculum to feed into the student model for data augmentation and guides the student to improve performance in a meta-learning style.
no code implementations • 8 Sep 2021 • Tianren Wang, Can Peng, Teng Zhang, Brian Lovell
With the excellent disentanglement properties of state-of-the-art generative models, image editing has been the dominant approach to control the attributes of synthesised face images.
no code implementations • 13 Jun 2020 • Tianren Wang, Teng Zhang, Brian Lovell
Text-to-Face (TTF) synthesis is a challenging task with great potential for diverse computer vision applications.
1 code implementation • CVPR 2020 • Sam Maksoud, Kun Zhao, Peter Hobson, Anthony Jennings, Brian Lovell
The difficulty of processing gigapixel whole slide images (WSIs) in clinical microscopy has been a long-standing barrier to implementing computer aided diagnostic systems.
no code implementations • 16 Jul 2019 • Liangchen Liu, Teng Zhang, Kun Zhao, Arnold Wiliem, Kieren Astin-Walmsley, Brian Lovell
We propose a novel two-stage zoom-in detection method to gradually focus on the object of interest.
no code implementations • 26 Apr 2016 • Johanna Carvajal, Arnold Wiliem, Conrad Sanderson, Brian Lovell
Can we predict the winner of Miss Universe after watching how they stride down the catwalk during the evening gown competition?
no code implementations • 4 Feb 2016 • Johanna Carvajal, Chris McCool, Brian Lovell, Conrad Sanderson
The final classification decision for each frame is then obtained by integrating the class probabilities at the frame level, which exploits the overlapping of the temporal windows.
no code implementations • 4 Feb 2016 • Johanna Carvajal, Arnold Wiliem, Chris McCool, Brian Lovell, Conrad Sanderson
We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation).
no code implementations • 30 Aug 2014 • Mehrtash Harandi, Richard Hartley, Brian Lovell, Conrad Sanderson
This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related areas.
no code implementations • 31 Jan 2014 • Mehrtash Harandi, Richard Hartley, Chunhua Shen, Brian Lovell, Conrad Sanderson
With the aim of building a bridge between the two realms, we address the problem of sparse coding and dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds.