Deep Q-learning for Active Recognition of GERMS: Baseline performance on a standardized dataset for active learning
نویسندگان
چکیده
Mohsen Malmir1 http://mplab.ucsd.edu/~mmalmir/ Karan Sikka1 http://mplab.ucsd.edu/~ksikka/ Deborah Forster1 [email protected] Javier Movellan2 http://www.emotient.com/ Garrison W. Cottrell3 http://cseweb.ucsd.edu/~gary/ 1 Machine Perception Lab. University of California San Diego, San Diego, CA, USA 2 Emotient, Inc. 4435 Eastgate Mall, Suite 320, San Diego, CA, USA 3 Computer Science and Engineering Dept. University of California San Diego, San Diego, CA, USA
منابع مشابه
MALMIR, SIKKA, FORSTER, MOVELLAN, COTTRELL: ACTIVE RECOGNITION OF GERMS1 Deep Q-learning for Active Recognition of GERMS: Baseline performance on a standardized dataset for active learning
In this paper, we introduce GERMS, a dataset designed to accelerate progress on active object recognition in the context of human robot interaction. GERMS consists of a collection of videos taken from the point of view of a humanoid robot that receives objects from humans and actively examines them. GERMS provides methods to simulate, evaluate, and compare active object recognition approaches t...
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تاریخ انتشار 2015