Factors of Influence for Transfer Learning Across Diverse Appearance Domains and Task Types.

Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision models, i.e.

A proto-object-based computational model for visual saliency.

State-of-the-art bottom-up saliency models often assign high saliency values at or near high-contrast edges, whereas people tend to look within the regions delineated by those edges, namely the objects. To resolve this inconsistency, in this work we estimate saliency at the level of coherent image regions. According to object-based attention theory, the human brain groups similar pixels into coherent regions, which are called proto-objects.