  This paper revisits visual saliency prediction by evaluating the recent
advancements in this field such as crowd-sourced mouse tracking-based databases
and contextual annotations. We pursue a critical and quantitative approach
towards some of the new challenges including the quality of mouse tracking
versus eye tracking for model training and evaluation. We extend quantitative
evaluation of models in order to incorporate contextual information by
proposing an evaluation methodology that allows accounting for contextual
factors such as text, faces, and object attributes. The proposed contextual
evaluation scheme facilitates detailed analysis of models and helps identify
their pros and cons. Through several experiments, we find that (1) mouse
tracking data has lower inter-participant visual congruency and higher
dispersion, compared to the eye tracking data, (2) mouse tracking data does not
totally agree with eye tracking in general and in terms of different contextual
regions in specific, and (3) mouse tracking data leads to acceptable results in
training current existing models, and (4) mouse tracking data is less reliable
for model selection and evaluation. The contextual evaluation also reveals
that, among the studied models, there is no single model that performs best on
all the tested annotations.
