DeepGUM presented at ECCV 2018

Our paper on robust regression was presented at ECCV’18 in Munich [1]. This paper presents a methodological framework for robust regression combining the representation power of deep architectures with the outlier detection capabilities of probabilistic models, in particular of a Gaussian-Uniform mixture (GUM). The code is available at https://github.com/Stephlat/DeepGUM. Abstract: In this paper we address the problem …

Audio-Visual Gaze Control

This project introduces a novel neural network-based reinforcement learning approach for robot gaze control. Our approach enables a robot to learn and to adapt its gaze control strategy for human-robot interaction neither with the use of external sensors nor with human supervision. The robot learns to focus its attention onto groups of people from its …

Deep Mixture of Linear Inverse Regressions (CVPR 2017)

Convolutional Neural Networks (ConvNets) have become the state-of-the-art for many classification and regression problems in computer vision. When it comes to regression, approaches such as measuring the Euclidean distance of target and predictions are often employed as output layer. In this paper, we propose the coupling of a Gaussian mixture of linear inverse regressions with …