Research Highlights
Out-of-Distribution Learning
Out-of-Distribution learning is concerned with developing methods where the distribution of the data processed at test time may be different from the distribution of the data used during the training of a model.
Immersive Analytics
Recent advanecs in augmented, mixed, and virtual reality, coupled with the need to perform analysis and decision-making on large-scale collections of volumetric images stimulate the research in immersive analytics.
Novelty and Anomaly Detection
Detecting the presence of outliers, like novelties or anomalies, with respect to a particular distribution has numerous applications in computer vision and in nearly every area of data science.
Recent Posts
Human Interactions
Recognizing human interactions from video is an important step forward towards the long-term goal of performing scene understanding fully automatically. Rece...
Dictionary Learning
Recent successes in the use of sparse coding for many computer vision applications have triggered the attention towards the problem of how an over-complete d...
Exemplar-based Object Layout
Recognizing the presence of object classes in an image, or image classification, has become an increasingly important topic of interest. Equally important, h...
Transfer Learning from Multiple Sources
Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the availab...