no code implementations • 5 Feb 2024 • Andrey Bryutkin, Jiahao Huang, Zhongying Deng, Guang Yang, Carola-Bibiane Schönlieb, Angelica Aviles-Rivero
We present a novel graph transformer framework, HAMLET, designed to address the challenges in solving partial differential equations (PDEs) using neural networks.
no code implementations • 30 Nov 2023 • Lihao Liu, Yanqi Cheng, Zhongying Deng, Shujun Wang, Dongdong Chen, Xiaowei Hu, Pietro Liò, Carola-Bibiane Schönlieb, Angelica Aviles-Rivero
Multi-object tracking in traffic videos is a crucial research area, offering immense potential for enhancing traffic monitoring accuracy and promoting road safety measures through the utilisation of advanced machine learning algorithms.
1 code implementation • 25 Jun 2023 • Jiahao Huang, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb, Guang Yang
Different from conventional diffusion models, the degradation operation of our CDiffMR is based on \textit{k}-space undersampling instead of adding Gaussian noise, and the restoration network is trained to harness a de-aliaseing function.
no code implementations • 23 Jan 2023 • Jiahao Huang, Angelica Aviles-Rivero, Carola-Bibiane Schonlieb, Guang Yang
The majority of existing deep learning models, e. g., convolutional neural networks, work on data with Euclidean or regular grids structures.
1 code implementation • 1 Dec 2020 • Rihuan Ke, Angelica Aviles-Rivero, Saurabh Pandey, Saikumar Reddy, Carola-Bibiane Schönlieb
The key idea of our technique is the extraction of the pseudo-masks statistical information to decrease uncertainty in the predicted probability whilst enforcing segmentation consistency in a multi-task fashion.
1 code implementation • 18 Nov 2020 • Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Hua Huang, Carola-Bibiane Schönlieb
In this work, we present a class of tuning-free PnP proximal algorithms that can determine parameters such as denoising strength, termination time, and other optimization-specific parameters automatically.
1 code implementation • ICML 2020 • Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Carola-Bibiane Schönlieb, Hua Huang
Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results.
1 code implementation • 15 Jan 2020 • Philip Sellars, Angelica Aviles-Rivero, Carola Bibiane Schönlieb
Demonstrating that direct implementation of the cluster assumption is a viable alternative to the popular consistency based regularisation.
no code implementations • 8 Dec 2019 • Yingda Yin, Qingnan Fan, Dong-Dong Chen, Yujie Wang, Angelica Aviles-Rivero, Ruoteng Li, Carola-Bibiane Schnlieb, Baoquan Chen
Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass.
1 code implementation • 14 Mar 2019 • Philip Sellars, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb
A central problem in hyperspectral image classification is obtaining high classification accuracy when using a limited amount of labelled data.
no code implementations • 14 Jan 2019 • Philip Sellars, Angelica Aviles-Rivero, Nicolas Papadakis, David Coomes, Anita Faul, Carola-Bibane Schönlieb
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification.