About Me

I am a PhD student at the Computer Science Department at Ben-Gurion University of the Negev (BGU), and a member of the Interdisciplinary Computational Vision Laboratory (iCVL) led by Prof. Ohad Ben-Shahar. My research is in the areas of computer vision, machine learning and human visual perception. I am interested in computation of bottom-up and top-down aspects of visual saliency and the intercation between the two.

Recent Achievements

  • Awarded the Negev-Faran research scholarship (2014)
  • Freidman Award for excellence in research, CS Dept., BGU (2014)
  • Zabey award for outstanding M.Sc. thesis in the faculty of natural sciences, BGU (2015)

Contact information

Rotem Mairon
Ben-Gurion University of The Negev
Beer-Sheva, Israel.
Office: Alon building, Vision Lab (37/-103)
Phone: +972-8-6477819
Email:

Teaching (TA):

Note:

The presentation files provided below are not formal course material,
and may not include everything discussed during the relevant session.
Formal practical sessions are uploaded to the course website.

Contextual Emergence of Object Saliency

Visual context is used in different forms for saliency computation. While its use in saliency models for fixations prediction is often reasoned, this is less so the case for approaches that aim to compute saliency at the object level.We argue that the types of context employed by these methods lack clear justification and may in fact interfere with the purpose of capturing the saliency of whole visual objects. In this paper we discuss the constraints that different types of context impose and suggest a new interpretation of visual context that allows the emergence of saliency for more complex, abstract, or multiple visual objects. Despite shying away from an explicit attempt to capture “objectness” (e.g., via segmentation), our results are qualitatively superior and quantitatively better previous art.

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Publications

Honors

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  • 2012 "Dance Your Ph.D" Contest winner
  • Member of the worlds largest multiple-player-single-guitar band
  • Generally awesome

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