Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt
Transferring the knowledge learned from large scale datasets (e.g., ImageNet) via fine-tuning offers an effective solution for domain-specific fine-grained visual categorization (FGVC) tasks (e.g., recognizing bird species or car make & model). In such scenarios, data annotation often calls for specialized domain knowledge and thus is difficult to scale. In this work, we first tackle a problem in large scale FGVC. Our method won first place in iNaturalist 2017 large scale species classification challenge. Central to the success of our approach is a training scheme that uses higher image resolution and deals with the long-tailed distribution of training data. Next, we study transfer learning via fine-tuning from large scale datasets to small scale, domain-specific FGVC datasets. We propose a measure to estimate domain similarity via Earth Mover's Distance and demonstrate that transfer learning benefits from pre-training on a source domain that is similar to the target domain by this measure. Our proposed transfer learning outperforms ImageNet pre-training and obtains state-of-the-art results on multiple commonly used FGVC datasets.
Originalsprog | Engelsk |
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Tidsskrift | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Sider (fra-til) | 4109-4118 |
Antal sider | 10 |
ISSN | 1063-6919 |
DOI | |
Status | Udgivet - 14 dec. 2018 |
Eksternt udgivet | Ja |
Begivenhed | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 - Salt Lake City, USA Varighed: 18 jun. 2018 → 22 jun. 2018 |
Konference
Konference | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
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Land | USA |
By | Salt Lake City |
Periode | 18/06/2018 → 22/06/2018 |
Bibliografisk note
Publisher Copyright:
© 2018 IEEE.
ID: 301825551