  Person Re-Identification is a challenging task that aims to retrieve all
instances of a query image across a system of non-overlapping cameras. Due to
the various extreme changes of view, it is common that local regions that could
be used to match people are suppressed, which leads to a scenario where
approaches have to evaluate the similarity of images based on less informative
regions. In this work, we introduce the Top-DB-Net, a method based on Top
DropBlock that pushes the network to learn to focus on the scene foreground,
with special emphasis on the most task-relevant regions and, at the same time,
encodes low informative regions to provide high discriminability. The
Top-DB-Net is composed of three streams: (i) a global stream encodes rich image
information from a backbone, (ii) the Top DropBlock stream encourages the
backbone to encode low informative regions with high discriminative features,
and (iii) a regularization stream helps to deal with the noise created by the
dropping process of the second stream, when testing the first two streams are
used. Vast experiments on three challenging datasets show the capabilities of
our approach against state-of-the-art methods. Qualitative results demonstrate
that our method exhibits better activation maps focusing on reliable parts of
the input images.
