no code implementations • 23 May 2017 • Talha Qaiser, Abhik Mukherjee, Chaitanya Reddy Pb, Sai Dileep Munugoti, Vamsi Tallam, Tomi Pitkäaho, Taina Lehtimäki, Thomas Naughton, Matt Berseth, Aníbal Pedraza, Ramakrishnan Mukundan, Matthew Smith, Abhir Bhalerao, Erik Rodner, Marcel Simon, Joachim Denzler, Chao-Hui Huang, Gloria Bueno, David Snead, Ian Ellis, Mohammad Ilyas, Nasir Rajpoot
In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring.
In this paper, we generalize average and bilinear pooling to "alpha-pooling", allowing for learning the pooling strategy during training.
In this paper, we study the sensitivity of CNN outputs with respect to image transformations and noise in the area of fine-grained recognition.
Neural networks and especially convolutional neural networks are of great interest in current computer vision research.
In the following paper, we present and discuss challenging applications for fine-grained visual classification (FGVC): biodiversity and species analysis.
Part models of object categories are essential for challenging recognition tasks, where differences in categories are subtle and only reflected in appearances of small parts of the object.
Classifying single image patches is important in many different applications, such as road detection or scene understanding.
Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination.