The Image-Quality Transfer (IQT) framework enhances low quality images by transferring information from high quality images acquired on expensive bespoke scanners. Although IQT has major potential in medical imaging, one key question is its dependence on training data. We demonstrate the generalisability of IQT used…
In the last decade, research in Computer Vision has developed several algorithms to help botanists and non-experts to classify plants based on images of their leaves. LeafSnap is a mobile application that uses a multiscale curvature model of the leaf margin to classify leaf images…
A growing number of commercial and enterprise systems increasingly rely on compute-intensive Machine Learning (ML) algorithms. While the demand for these compute-intensive applications is growing, the performance benefits from general-purpose platforms are diminishing. Field Programmable Gate Arrays (FPGAs) provide a promising path forward to accommodate…
This paper investigates the connections between two state of the art classifiers: decision forests (DFs, including decision jungles) and convolutional neural networks (CNNs). Decision forests are computationally efficient thanks to their conditional computation property (computation is confined to only a small region of the tree,…
We consider a multiple-hypothesis testing setting where the hypotheses are ordered and one is only permitted to reject an initial contiguous block of hypotheses. A rejection rule in this setting amounts to a procedure for choosing the stopping point k. This setting is inspired by…
Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension. Often there exist features that are locally translation invariant and would be valuable for directing the model’s attention, but previous attentional architectures are not…
Bezigons, i.e., closed paths composed of Bezier curves, have been widely employed to describe shapes in image vectorization results. However, most existing vectorization techniques infer the bezigons by simply approximating an intermediate vector representation (such as polygons). Consequently, the resultant bezigons are sometimes imperfect due…
Latency to end-users and regulatory requirements push large companies to build data centers all around the world. The resulting data is “born” geographically distributed. On the other hand, many Machine Learning applications require a global view of such data in order to achieve the best…
To break down the geometry assumptions of traditional motion models (e.g., homography, affine), warping-based motion model recently becomes very popular and is adopted in many latest applications (e.g., image stitching, video stabilization). With high degrees of freedom, the accuracy of model heavily relies on data-terms…
With the growing popularity of short-form video sharing platforms such as Instagram and Vine, there has been an increasing need for techniques that automatically extract highlights from video. Whereas prior works have approached this problem with heuristic rules or supervised learning, we present an unsupervised…
We present a novel system to help visually impaired people to move efficiently and safely in indoor environments by mapping input from a depth camera to spatially localized auditory cues. We propose a set of context-specific cues which are suitable for use in systems that…
We present Deep Neural Decision Forests – a novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner. To combine these two worlds, we introduce a stochastic and differentiable decision tree model,…
