Video analysis plays an important role in the field of computer vision and finds its application in many areas. Fine-grained event classification is one of the most chall[...]

Understanding and characterizing cancer patient outcomes is challenging and involves multiple clinical measurements (e.g., imaging and genomics biomarkers). Enabling mult[...]

The availability of large amounts of labeled training data is a major contributing factor (and a bottleneck) to the recent progress in the field of Deep Learning. However[...]

Recent work in image manifold learning has shown the prevalence of unsupervised methods that provide compact representation and perceptually meaningful organization of im[...]

Multiple object tracking is a fundamental problem within computer vision and has a wide range of applications. Although well studied, it remains a difficult task especia[...]

To leverage large-scale collaboratively-tagged (loosely-tagged) images for training a large number of classifiers to support large-scale image classification, we need to [...]

The identification of the artist of a painting is also known as art authentication, and the answer to this question is manifest through art gallery exhibition and is rein[...]

A key goal in robotics is to enable autonomous motion for mobile robots in a real world dynamic environment with unknown obstacles. This problem requires bringing togethe[...]

Feedback driven deep reinforcement learning methodologies are widely favoured approaches to solving artificial intelligence problems. The algorithms navigate complex deci[...]

To automatically classify biological images, machine learning techniques have been widely used to train the classifiers from labeled images. For a new category of biologi[...]