  Unsupervised domain adaptive (UDA) person re-identification (ReID) has gained
increasing attention for its effectiveness on the target domain without manual
annotations. Most fine-tuning based UDA person ReID methods focus on encoding
global features for pseudo labels generation, neglecting the local feature that
can provide for the fine-grained information. To handle this issue, we propose
a Learning Feature Fusion (LF2) framework for adaptively learning to fuse
global and local features to obtain a more comprehensive fusion feature
representation. Specifically, we first pre-train our model within a source
domain, then fine-tune the model on unlabeled target domain based on the
teacher-student training strategy. The average weighting teacher network is
designed to encode global features, while the student network updating at each
iteration is responsible for fine-grained local features. By fusing these
multi-view features, multi-level clustering is adopted to generate diverse
pseudo labels. In particular, a learnable Fusion Module (FM) for giving
prominence to fine-grained local information within the global feature is also
proposed to avoid obscure learning of multiple pseudo labels. Experiments show
that our proposed LF2 framework outperforms the state-of-the-art with 73.5% mAP
and 83.7% Rank1 on Market1501 to DukeMTMC-ReID, and achieves 83.2% mAP and
92.8% Rank1 on DukeMTMC-ReID to Market1501.
