Clinton Fookes

TL;DR: An approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes is proposed.

TL;DR: It is shown that the off-the-shelf CNN features, while originally trained for classifying generic objects, are also extremely good at representing iris images, effectively extracting discriminative visual features and achieving promising recognition results on two iris datasets: ND-CrossSensor-2013 and CASIA-Iris-Thousand.

TL;DR: This work proposes a novel method to predict the future motion of a pedestrian given a short history of their, and their neighbours, past behaviour using a combined attention model which utilises both "soft attention" as well as "hard-wired" attention in order to map the trajectory information from the local neighbourhood to the future positions of the pedestrian of interest.

TL;DR: In this paper, a combined attention model was proposed to map the trajectory information from the local neighbourhood to the future positions of the pedestrian of interest, where a simple approximation of attention weights (i.e hard-wired) can be merged together with soft attention weights in order to make their model applicable for challenging real world scenarios with hundreds of neighbours.

TL;DR: This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.