Towards Balanced Learning for Instance Recognition
Towards Balanced Learning for Instance Recognition
About this item
Full title
Author / Creator
Pang, Jiangmiao , Chen, Kai , Li, Qi , Xu, Zhihai , Feng, Huajun , Shi, Jianping , Ouyang, Wanli and Lin, Dahua
Publisher
New York: Springer US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Springer US
Subjects
More information
Scope and Contents
Contents
Instance recognition is rapidly advanced along with the developments of deep convolutional neural networks. Compared to the model architectures the training process, which is also crucial to the success of detectors, has received relatively less attention. In this work, we carefully revisit the standard training practice of detectors, and find that...
Alternative Titles
Full title
Towards Balanced Learning for Instance Recognition
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2522240059
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2522240059
Other Identifiers
ISSN
0920-5691
E-ISSN
1573-1405
DOI
10.1007/s11263-021-01434-2
