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Chapter and Conference Paper
FEAR: Fast, Efficient, Accurate and Robust Visual Tracker
We present FEAR, a family of fast, efficient, accurate, and robust Siamese visual trackers. We present a novel and efficient way to benefit from dual-template representation for object model adaption, which incor...
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Chapter and Conference Paper
Repeatability Is Not Enough: Learning Affine Regions via Discriminability
A method for learning local affine-covariant regions is presented. We show that maximizing geometric repeatability does not lead to local regions, a.k.a features, that are reliably matched and this necessitate...
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Chapter and Conference Paper
The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results
The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned mo...
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Chapter and Conference Paper
The Visual Object Tracking VOT2016 Challenge Results
The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a l...
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Chapter and Conference Paper
Relevance Assessment for Visual Video Re-ranking
The following problem is considered: Given a name or phrase specifying an object, collect images and videos from the internet possibly depicting the object using a textual query on their name or annotation. A ...
