Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Activity recognition aims to provide accurate and opportune information on people's
activities by leveraging sensory data available in today's sensory rich environments …

Object class detection, also known as category-level object detection, has become one of
the most focused areas in computer vision in the new century. This article attempts to …

We investigate omni-supervised learning, a special regime of semi-supervised learning in
which the learner exploits all available labeled data plus internet-scale sources of unlabeled …

Deep ConvNets have shown great performance for single-label image classification (eg
ImageNet), but it is necessary to move beyond the single-label classification task because …

Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That
said, while in classical (ie, non-deep) clustering the benefits of the nonparametric approach …

The past years have witnessed great progress on remote sensing (RS) image interpretation
and its wide applications. With RS images becoming more accessible than ever before …

With the of advent rich classification models and high computational power visual
recognition systems have found many operational applications. Recognition in the real …

Supervised learning in large discriminative models is a mainstay for modern computer
vision. Such an approach necessitates investing in large-scale human-annotated datasets …

Convolutional networks trained on large supervised datasets produce visual features which
form the basis for the state-of-the-art in many computer-vision problems. Further …