  This paper studies the problem of Person Re-Identification (ReID)for
large-scale applications. Recent research efforts have been devoted to building
complicated part models, which introduce considerably high computational cost
and memory consumption, inhibiting its practicability in large-scale
applications. This paper aims to develop a novel learning strategy to find
efficient feature embeddings while maintaining the balance of accuracy and
model complexity. More specifically, we find by enhancing the classical triplet
loss together with cross-entropy loss, our method can explore the hard examples
and build a discriminant feature embedding yet compact enough for large-scale
applications. Our method is carried out progressively using Bayesian
optimization, and we call it the Progressive Learning Algorithm (PLA).
Extensive experiments on three large-scale datasets show that our PLA is
comparable or better than the-state-of-the-arts. Especially, on the challenging
Market-1501 dataset, we achieve Rank-1=94.7\%/mAP=89.4\% while saving at least
30\% parameters than strong part models.
