Research Highlights

Out-of-Distribution Learning

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.

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Immersive Analytics

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.

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Novelty and Anomaly Detection

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.

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Recent Posts

Dictionary Learning

2 minute read

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

2 minute read

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

2 minute read

Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the availab...

Video Surveillance

7 minute read

Increasingly, large networks of surveillance cameras are employed to monitor public and private facilities. This continuous collection of imagery has the pot...