"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","157" "Title:","Object Localization based on Structural SVM using Privileged Information" Current Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The method is effective for the object localization task and results in good improvements in localization accuracy. It looks like the authors' formulation of SSVM+ contains separate slack variables \xi_i for each example x_i and there are extra degrees of freedom. How many alternating iterations are required? When the parameter vectors w and w^* are far from the optimal solution, could this alternating inference procedure get stuck in bad local minima?

In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making. Specifically, such modules need to estimate the probability of each predicted object in a given region and the confidence interval for its bounding box. While recent Bayesian deep learning methods provide a principled way to estimate this uncertainty, the estimates for the bounding boxes obtained using these methods are uncalibrated. In this paper, we address this problem for the single-object localization task by adapting an existing technique for calibrating regression models. We show, experimentally, that the resulting calibrated model obtains more reliable uncertainty estimates.