In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

Matching pedestrians across multiple camera views, known as human re-identification, is a
challenging research problem that has numerous applications in visual surveillance. With …

Real-time video objects detection, tracking, and recognition are challenging issues due to
the real-time processing requirements of the machine learning algorithms. In recent years …

In this paper, we tackle the problem of RGB-D semantic segmentation of indoor images. We
take advantage of deconvolutional networks which can predict pixel-wise class labels, and …

In this paper, we propose an approach to learn hierarchical features for visual object
tracking. First, we offline learn features robust to diverse motion patterns from auxiliary video …

In image labeling, local representations for image units (pixels, patches or superpixels) are
usually generated from their surrounding image patches, thus long-range contextual …

In this paper, we address the challenging task of scene segmentation. In order to capture the
rich contextual dependencies over image regions, we propose Directed Acyclic Graph …

Deep learning has recently achieved very promising results in a wide range of areas such
as computer vision, speech recognition and natural language processing. It aims to learn …

We propose a novel method for semantic segmentation, the task of labeling each pixel in an
image with a semantic class. Our method combines the advantages of the two main …

Variational Level Set (LS) has been a widely used method in medical segmentation.
However, it is limited when dealing with multi-instance objects in the real world. In addition …