Antonio Torralba

TL;DR: A saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task and applies to improve existing networks for the tasks of human gaze estimation and fine-grained object classification.

TL;DR: A novel approach to the problem of localizing objects in an image and estimating their fine-pose by proposing FPM, a fine pose parts-based model that combines geometric information in the form of shared 3D parts in deformable part based models, and appearance information inthe form of objectness to achieve both fast and accurate fine pose estimation.

TL;DR: In this article, a hierarchical Bayesian model is proposed to transfer knowledge from previously learned categories to a novel category, in the form of a prior over category means and variances, which can discover how to group categories into meaningful super-categories that express different priors for new classes.

TL;DR: SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects.

TL;DR: In this article, a random lens is defined as one for which the function relating the input light ray to the output sensor location is pseudo-random, and two machine learning methods are compared for both camera calibration and image reconstruction.