The rapid expansions of urban rail networks are faced with the growing number of
disruptions caused by the complex rail signaling systems, incorrect driving behaviors, and …

Current attention algorithms (eg, self-attention) are stimulus-driven and highlight all the
salient objects in an image. However, intelligent agents like humans often guide their …

Several visual attention models have been proposed for describing eye movements over
simple stimuli and tasks such as free viewing or visual search. Yet, to date, there exists no …

Computational models of visual attention have become popular over the past decade, we
believe primarily for two reasons: First, models make testable predictions that can be …

Transformer attention plays an important role in current top-performing trackers. However, it
is bottom-up, driven by stimulus and lacks intrinsic prior guidance. This bottom-up attention …

Volitional signals for gaze control are provided by multiple parallel pathways converging on
the midbrain superior colliculus (SC), whose deeper layers output to the brainstem gaze …

We present a method for estimating human scanpaths, which are sequences of gaze shifts
that follow visual attention over an image. In this work, scanpaths are modeled based on …

We address the question of inferring the search target from fixation behavior in visual
search. Such inference is possible since during search, our attention and gaze are guided …

Attention detection is important for many applications. Automatic determination of users'
visual attention state is challenging because attention involves numerous complex and …

In this Chapter we consider eye movements and, in particular, the resulting sequence of
gaze shifts to be the observable outcome of a stochastic process. Crucially, we show that …