  Multi-grained features extracted from convolutional neural networks (CNNs)
have demonstrated their strong discrimination ability in supervised person
re-identification (Re-ID) tasks. Inspired by them, this work investigates the
way of extracting multi-grained features from a pure transformer network to
address the unsupervised Re-ID problem that is label-free but much more
challenging. To this end, we build a dual-branch network architecture based
upon a modified Vision Transformer (ViT). The local tokens output in each
branch are reshaped and then uniformly partitioned into multiple stripes to
generate part-level features, while the global tokens of two branches are
averaged to produce a global feature. Further, based upon offline-online
associated camera-aware proxies (O2CAP) that is a top-performing unsupervised
Re-ID method, we define offline and online contrastive learning losses with
respect to both global and part-level features to conduct unsupervised
learning. Extensive experiments on three person Re-ID datasets show that the
proposed method outperforms state-of-the-art unsupervised methods by a
considerable margin, greatly mitigating the gap to supervised counterparts.
Code will be available soon at https://github.com/RikoLi/WACV23-workshop-TMGF.
