  This PhD. Thesis concerns the study and development of hierarchical
representations for spatio-temporal visual attention modeling and understanding
in video sequences. More specifically, we propose two computational models for
visual attention. First, we present a generative probabilistic model for
context-aware visual attention modeling and understanding. Secondly, we develop
a deep network architecture for visual attention modeling, which first
estimates top-down spatio-temporal visual attention, and ultimately serves for
modeling attention in the temporal domain.
