  Recently, with the advance of deep Convolutional Neural Networks (CNNs),
person Re-Identification (Re-ID) has witnessed great success in various
applications. However, with limited receptive fields of CNNs, it is still
challenging to extract discriminative representations in a global view for
persons under non-overlapped cameras. Meanwhile, Transformers demonstrate
strong abilities of modeling long-range dependencies for spatial and sequential
data. In this work, we take advantages of both CNNs and Transformers, and
propose a novel learning framework named Hierarchical Aggregation Transformer
(HAT) for image-based person Re-ID with high performance. To achieve this goal,
we first propose a Deeply Supervised Aggregation (DSA) to recurrently aggregate
hierarchical features from CNN backbones. With multi-granularity supervisions,
the DSA can enhance multi-scale features for person retrieval, which is very
different from previous methods. Then, we introduce a Transformer-based Feature
Calibration (TFC) to integrate low-level detail information as the global prior
for high-level semantic information. The proposed TFC is inserted to each level
of hierarchical features, resulting in great performance improvements. To our
best knowledge, this work is the first to take advantages of both CNNs and
Transformers for image-based person Re-ID. Comprehensive experiments on four
large-scale Re-ID benchmarks demonstrate that our method shows better results
than several state-of-the-art methods. The code is released at
https://github.com/AI-Zhpp/HAT.
