Hardness-Aware Deep Metric Learning

CVPR 2019  ·  Wenzhao Zheng, Zhaodong Chen, Jiwen Lu, Jie zhou ·

This paper presents a hardness-aware deep metric learning (HDML) framework. Most previous deep metric learning methods employ the hard negative mining strategy to alleviate the lack of informative samples for training. However, this mining strategy only utilizes a subset of training data, which may not be enough to characterize the global geometry of the embedding space comprehensively. To address this problem, we perform linear interpolation on embeddings to adaptively manipulate their hard levels and generate corresponding label-preserving synthetics for recycled training, so that information buried in all samples can be fully exploited and the metric is always challenged with proper difficulty. Our method achieves very competitive performance on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets.

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Results from the Paper

Ranked #26 on Metric Learning on CUB-200-2011 (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Metric Learning CARS196 GoogLeNet + HDML R@1 79.1 # 27
Metric Learning CUB-200-2011 GoogLeNet + HDML R@1 53.7 # 26


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