ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images. We revisit ProxyNCA and incorporate several enhancements. We find that low temperature scaling is a performance-critical component and explain why it works. Besides,...
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ftdatacite:10.48550/arxiv.2004.01113 2023-05-15T16:01:55+02:00 ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis Teh, Eu Wern DeVries, Terrance Taylor, Graham W. 2020 https://dx.doi.org/10.48550/arxiv.2004.01113 https://arxiv.org/abs/2004.01113 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Article CreativeWork article Preprint 2020 ftdatacite https://doi.org/10.48550/arxiv.2004.01113 2022-03-10T16:15:06Z We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images. We revisit ProxyNCA and incorporate several enhancements. We find that low temperature scaling is a performance-critical component and explain why it works. Besides, we also discover that Global Max Pooling works better in general when compared to Global Average Pooling. Additionally, our proposed fast moving proxies also addresses small gradient issue of proxies, and this component synergizes well with low temperature scaling and Global Max Pooling. Our enhanced model, called ProxyNCA++, achieves a 22.9 percentage point average improvement of Recall@1 across four different zero-shot retrieval datasets compared to the original ProxyNCA algorithm. Furthermore, we achieve state-of-the-art results on the CUB200, Cars196, Sop, and InShop datasets, achieving Recall@1 scores of 72.2, 90.1, 81.4, and 90.9, respectively. : To appear in the European Conference on Computer Vision (ECCV) 2020 Article in Journal/Newspaper DML DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
spellingShingle |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Teh, Eu Wern DeVries, Terrance Taylor, Graham W. ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis |
topic_facet |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
description |
We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images. We revisit ProxyNCA and incorporate several enhancements. We find that low temperature scaling is a performance-critical component and explain why it works. Besides, we also discover that Global Max Pooling works better in general when compared to Global Average Pooling. Additionally, our proposed fast moving proxies also addresses small gradient issue of proxies, and this component synergizes well with low temperature scaling and Global Max Pooling. Our enhanced model, called ProxyNCA++, achieves a 22.9 percentage point average improvement of Recall@1 across four different zero-shot retrieval datasets compared to the original ProxyNCA algorithm. Furthermore, we achieve state-of-the-art results on the CUB200, Cars196, Sop, and InShop datasets, achieving Recall@1 scores of 72.2, 90.1, 81.4, and 90.9, respectively. : To appear in the European Conference on Computer Vision (ECCV) 2020 |
format |
Article in Journal/Newspaper |
author |
Teh, Eu Wern DeVries, Terrance Taylor, Graham W. |
author_facet |
Teh, Eu Wern DeVries, Terrance Taylor, Graham W. |
author_sort |
Teh, Eu Wern |
title |
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis |
title_short |
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis |
title_full |
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis |
title_fullStr |
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis |
title_full_unstemmed |
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis |
title_sort |
proxynca++: revisiting and revitalizing proxy neighborhood component analysis |
publisher |
arXiv |
publishDate |
2020 |
url |
https://dx.doi.org/10.48550/arxiv.2004.01113 https://arxiv.org/abs/2004.01113 |
genre |
DML |
genre_facet |
DML |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2004.01113 |
_version_ |
1766397598172708864 |