no code implementations • 17 Nov 2022 • Zhongying Deng, Yanqi Chen, Lihao Liu, Shujun Wang, Rihuan Ke, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero
Firstly, TrafficCAM provides both pixel-level and instance-level semantic labelling along with a large range of types of vehicles and pedestrians.
1 code implementation • 17 Nov 2022 • Zhongying Deng, Rihuan Ke, Carola-Bibiane Schonlieb, Angelica I Aviles-Rivero
Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set and massive amounts of unlabeled data.
no code implementations • 19 Sep 2022 • Rihuan Ke
With recent deep learning based approaches showing promising results in removing noise from images, the best denoising performance has been reported in a supervised learning setup that requires a large set of paired noisy images and ground truth for training.
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 • 14 Aug 2020 • Rihuan Ke, Carola-Bibiane Schönlieb
The ground truth images, however, are often unavailable or very expensive to acquire in real-world applications.
no code implementations • 26 May 2020 • Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Mark Kirkland, Peter Schuetz, Carola-Bibiane Schönlieb
The subproblems are handled by a framework that consists of 1) a segmentation task that learns from pixel-level ground truth segmentation masks of a small fraction of the images, 2) a recursive approximation task that conducts partial object regions learning and data-driven mask evolution starting from partial masks of each object instance, and 3) other problem oriented auxiliary tasks that are trained with sparse annotations and promote the learning of dedicated features.
2 code implementations • 11 May 2020 • Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb
U-Nets have been established as a standard architecture for image-to-image learning problems such as segmentation and inverse problems in imaging.
no code implementations • 25 Sep 2019 • Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Peter Schuetz, Carola-Bibiane Schönlieb
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for image segmentation.
no code implementations • 20 Jun 2019 • Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Peter Schuetz, Carola-Bibiane Schönlieb
In this paper, we introduce a deep convolutional neural network for microscopy image segmentation.