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.
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.
Segmenting the image plane of video sequences is often one of the first steps towards the analysis of video. A lot of effort has been spent on developing ima...
Recognition of objects based on their images is one of the central problems in modern Computer Vision. Objects can be characterized by their geometric, photo...