We propose a new learning method which exploits temporal consistency to successfully learn a complex appearance model from a sparsely labeled training video. Our approach consists...
We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from wh...
Virtually all methods in image processing and computer vision, for removing weather effects from images, assume single scattering of light by particles in the atmosphere. In reali...
In the traditional software engineering courses, the students develop small programs from scratch. This does not correspond to industry practice where programmers spend most of th...
Joseph Buchta, Maksym Petrenko, Denys Poshyvanyk, ...