# Hierarchical Representations for Spatio-Temporal Visual Attention Modeling and Understanding

arXiv: https://arxiv.org/abs/2308.05189 · HTML: https://mlsanity.com/paper/2308.05189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2023.

## Abstract (arXiv)

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.