Saliency Revisited: Analysis of Mouse Movements versus Fixations

Understand

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.