This work presents a new approach to discriminative speaker verification. Rather than estimating speaker models, or a model that discriminates between a speaker class and the cla...
This paper tackles the problem of fitting multiple instances of a model to data corrupted by noise and outliers. The proposed solution is based on random sampling and conceptual da...
Adaptive background modeling/subtraction techniques are popular, in particular, because they are able to cope with background variations that are due to lighting variations. Unfor...
Leonid Taycher, John W. Fisher III, Trevor Darrell
The enormous number of questions needed to acquire a full preference model when the size of the outcome space is large forces us to work with partial models that approximate the u...
We address the problem of temporal unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the applica...
Dong Zhang, Daniel Gatica-Perez, Samy Bengio, Iain...