  Many of the existing Person Re-identification (Re-ID) approaches depend on
feature maps which are either partitioned to localize parts of a person or
reduced to create a global representation. While part localization has shown
significant success, it uses either na{\i}ve position-based partitions or
static feature templates. These, however, hypothesize the pre-existence of the
parts in a given image or their positions, ignoring the input image-specific
information which limits their usability in challenging scenarios such as Re-ID
with partial occlusions and partial probe images. In this paper, we introduce a
spatial attention-based Dynamic Part Template Initialization module that
dynamically generates part-templates using mid-level semantic features at the
earlier layers of the backbone. Following a self-attention layer, human
part-level features of the backbone are used to extract the templates of
diverse human body parts using a simplified cross-attention scheme which will
then be used to identify and collate representations of various human parts
from semantically rich features, increasing the discriminative ability of the
entire model. We further explore adaptive weighting of part descriptors to
quantify the absence or occlusion of local attributes and suppress the
contribution of the corresponding part descriptors to the matching criteria.
Extensive experiments on holistic, occluded, and partial Re-ID task benchmarks
demonstrate that our proposed architecture is able to achieve competitive
performance. Codes will be included in the supplementary material and will be
made publicly available.
