D15-1224 real-world uncertainty , such object recognition errors or cluttered scenes .
C92-4200 Torino . 3.1 OBJECT RECOGNITION The object recognition step processes the text from
J86-1012 approach to three-dimensional object recognition from a single view . The system
C92-4200 pragmatical reasons . When the object recognition analysis reported some problems
C92-4200 failures were reported during the object recognition . It integrates Bottom-Up ( BUS
D14-1005 well , but apply them to a visual object recognition task instead of concept meaning
C92-4200 the University of Torino . 3.1 OBJECT RECOGNITION The object recognition step processes
C92-4200 order to pertbrm two main steps : object recognition and object linking . This separation
A97-1002 hard/software to allow visual object recognition for lexical acquisition . <title>
D14-1005 CNN ) trained on a large labeled object recognition dataset . This transfer learning
C92-4200 syntax and semantics brings to the object recognition from a structural and semantic
D13-1038 right ) . In addition , we use object recognition models ( Zhang and Lu , 2002
H05-1064 describe a hidden-variable model for object recognition in computer vision . The approaches
D12-1019 recognition ( Rabiner , 1989 ) and object recognition ( Quattoni et al. , 2004 ) .
D15-1015 the network has been trained for object recognition . If , however , we are interested
D15-1015 computer vision tasks such as object recognition ( Razavian et al. , 2014 ) .
C92-4200 sentence are identified by the object recognition step , a connection among them
C92-4200 . is guarantied because at the object recognition level each syntactic connection
D13-1115 have been immensely successful in object recognition ( Farhadi et al. , 2009 ) , act
C92-4200 to the reliability rate of the object recognition . Note that even the TDS+BUS
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