Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/111362
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Type: Conference paper
Title: Smart mining for deep metric learning
Author: Harwood, B.
Kumar, V.
Carneiro, G.
Reid, I.
Drummond, T.
Citation: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017), 2017 / vol.2017-October, pp.2840-2848
Publisher: IEEE
Publisher Place: Piscataway, NJ
Issue Date: 2017
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 9781538610336
ISSN: 1550-5499
Conference Name: IEEE International Conference on Computer Vision (ICCV 2017) (22 Oct 2017 - 29 Oct 2017 : Venice, ITALY)
Statement of
Responsibility: 
Ben Harwood, Vijay Kumar B G, Gustavo Carneiro, Ian Reid, Tom Drummond
Abstract: To solve deep metric learning problems and producing feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples from the same class and maximise the relative distance between samples from different classes. Though successful, the training convergence of this triplet model can be compromised by the fact that the vast majority of the training samples will produce gradients with magnitudes that are close to zero. This issue has motivated the development of methods that explore the global structure of the embedding and other methods that explore hard negative/positive mining. The effectiveness of such mining methods is often associated with intractable computational requirements. In this paper, we propose a novel deep metric learning method that combines the triplet model and the global structure of the embedding space. We rely on a smart mining procedure that produces effective training samples for a low computational cost. In addition, we propose an adaptive controller that automatically adjusts the smart mining hyper-parameters and speeds up the convergence of the training process. We show empirically that our proposed method allows for fast and more accurate training of triplet ConvNets than other competing mining methods. Additionally, we show that our method achieves new state-of-the-art embedding results for CUB-200-2011 and Cars196 datasets.
Rights: © 2017 IEEE
RMID: 0030083145
DOI: 10.1109/ICCV.2017.307
Grant ID: http://purl.org/au-research/grants/arc/CE140100016
http://purl.org/au-research/grants/arc/FL130100102
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8234942
Appears in Collections:Computer Science publications

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