This paper investigates a novel problem of generating images from visual attributes. We
model the image as a composite of foreground and background and develop a layered …

INTRODUCTION Compositionality, generalization, and learning from a few examples are
among the hallmarks of human intelligence. CAPTCHAs (Completely Automated Public …

Instance segmentation is the problem of detecting and delineating each distinct object of
interest appearing in an image. Current instance segmentation approaches consist of …

We address the problem of learning object class models and object segmentations from
unannotated images. We introduce LOCUS (learning object classes with unsupervised …

A good model of object shape is essential in applications such as segmentation, detection,
inpainting and graphics. For example, when performing segmentation, local constraints on …

While Boltzmann Machines have been successful at unsupervised learning and density
modeling of images and speech data, they can be very sensitive to noise in the data. In this …

Learning object-centric representations of multi-object scenes is a promising approach
towards machine intelligence, facilitating high-level reasoning and control from visual …

We present an unsupervised approach for learning a layered representation of a scene from
a video for motion segmentation. Our method is applicable to any video containing …

Amodal completion is a visual task that humans perform easily but which is difficult for
computer vision algorithms. The aim is to segment those object boundaries which are …

Computer vision has grown tremendously in the past two decades. Despite all efforts,
existing attempts at matching parts of the human visual system's extraordinary ability to …