  Deep metric learning aims to learn a deep embedding that can capture the
semantic similarity of data points. Given the availability of massive training
samples, deep metric learning is known to suffer from slow convergence due to a
large fraction of trivial samples. Therefore, most existing methods generally
resort to sample mining strategies for selecting nontrivial samples to
accelerate convergence and improve performance. In this work, we identify two
critical limitations of the sample mining methods, and provide solutions for
both of them. First, previous mining methods assign one binary score to each
sample, i.e., dropping or keeping it, so they only selects a subset of relevant
samples in a mini-batch. Therefore, we propose a novel sample mining method,
called Online Soft Mining (OSM), which assigns one continuous score to each
sample to make use of all samples in the mini-batch. OSM learns extended
manifolds that preserve useful intraclass variances by focusing on more similar
positives. Second, the existing methods are easily influenced by outliers as
they are generally included in the mined subset. To address this, we introduce
Class-Aware Attention (CAA) that assigns little attention to abnormal data
samples. Furthermore, by combining OSM and CAA, we propose a novel weighted
contrastive loss to learn discriminative embeddings. Extensive experiments on
two fine-grained visual categorisation datasets and two video-based person
re-identification benchmarks show that our method significantly outperforms the
state-of-the-art.
