  Person re-identification (ReID) aims at re-identifying persons from different
viewpoints across multiple cameras, of which it is of great importance to learn
multifaceted features expressed in different parts of a person, e.g., clothes,
bags, and other accessories in the main body, appearance in the head, and shoes
in the foot. To learn such features, existing methods are focused on the
striping-based approach that builds multi-branch neural networks to learn local
features in each part of the identities, with one-branch network dedicated to
one part. This results in complex models with a large number of parameters. To
address this issue, this paper proposes to learn the multifaceted features in a
simple unified single-branch neural network. The Unified Multifaceted Feature
Learning (UMFL) framework is introduced to fulfill this goal, which consists of
two key collaborative modules: compound batch image erasing (including batch
constant erasing and random erasing) and hierarchical structured loss. The loss
structures the augmented images resulted by the two types of image erasing in a
two-level hierarchy and enforces multifaceted attention to different parts. As
we show in the extensive experimental results on four benchmark person ReID
datasets, despite the use of significantly simplified network structure, our
method performs substantially better than state-of-the-art competing methods.
Our method can also effectively generalize to vehicle ReID, achieving similar
improvement on two vehicle ReID datasets.
