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GRASP Special Seminar: Ryan Farrell, Brigham Young University, “Fine-grained Recognition using Pose-normalization and Captured Domain Models”

May 20, 2015 @ 2:00 pm - 3:00 pm


 While humans can readily identify basic-level categories such as table, turtle, or trumpet, recognition of subordinate-level categories within a domain (e.g. species of birds or make/model/year of vehicles) is very difficult and typically requires extensive experience or expertise with a given domain.  To date, research efforts to develop computational approaches for the recognition of such subordinate or “fine-grained” categories have largely sought to apply the same techniques used for basic-level recognition, only on a larger scale (more categories).

 In this talk, I will describe directions that we are currently pursuing in my lab to address the specific challenges inherent in fine-grained recognition.  The key underlying paradigm is a pose-normalized representation which pairs a captured domain-level model of geometry with category-specific appearance models.  This representation enables objects to be perceived independent of pose, articulation or viewing angle.  Distinguishing features are learned and recognition is performed in this pose-normalized space.  I will conclude by discussing the integration of human domain expertise into computational models and the diverse applications of fine-grained recognition.


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Ryan Farrell is an Assistant Professor in the Computer Science department at Brigham Young University (BYU). He previously worked as a research scientist at the International Computer Science Institute (ICSI), a non-profit research institute affiliated with UC Berkeley, having completed his master’s and doctorate degrees at the University of Maryland, College Park.


May 20, 2015
2:00 pm - 3:00 pm
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