  In person re-identification (re-ID), extracting part-level features from
person images has been verified to be crucial to offer fine-grained
information. Most of the existing CNN-based methods only locate the human parts
coarsely, or rely on pretrained human parsing models and fail in locating the
identifiable nonhuman parts (e.g., knapsack). In this article, we introduce an
alignment scheme in transformer architecture for the first time and propose the
auto-aligned transformer (AAformer) to automatically locate both the human
parts and nonhuman ones at patch level. We introduce the "Part tokens
([PART]s)", which are learnable vectors, to extract part features in the
transformer. A [PART] only interacts with a local subset of patches in
self-attention and learns to be the part representation. To adaptively group
the image patches into different subsets, we design the auto-alignment.
Auto-alignment employs a fast variant of optimal transport (OT) algorithm to
online cluster the patch embeddings into several groups with the [PART]s as
their prototypes. AAformer integrates the part alignment into the
self-attention and the output [PART]s can be directly used as part features for
retrieval. Extensive experiments validate the effectiveness of [PART]s and the
superiority of AAformer over various state-of-the-art methods.
