Part-based R-CNNs for fine-grained category detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8689 LNCS, Issue PART 1, 2014, Pages 834-849

  Zhang, Ning ^a   Donahue, Jeff ^a   Girshick, Ross ^a   Darrell, Trevor ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

convolutional models; Fine grained recognition; object detection

Indexed keywords

CONVOLUTION; OBJECT RECOGNITION; SEMANTICS;

BOUNDING BOX; CATEGORIZATION METHODS; CONVOLUTIONAL MODEL; FINE-GRAINED RECOGNITION; GEOMETRIC CONSTRAINT; OBJECT DETECTION; SEMANTIC PARTS; TEST TIME;

STATISTICAL TESTS;

EID: 84906514027     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10590-1_54     Document Type: Conference Paper

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