  Advanced deep Convolutional Neural Networks (CNNs) have shown great success
in video-based person Re-Identification (Re-ID). However, they usually focus on
the most obvious regions of persons with a limited global representation
ability. Recently, it witnesses that Transformers explore the inter-patch
relations with global observations for performance improvements. In this work,
we take both sides and propose a novel spatial-temporal complementary learning
framework named Deeply-Coupled Convolution-Transformer (DCCT) for
high-performance video-based person Re-ID. Firstly, we couple CNNs and
Transformers to extract two kinds of visual features and experimentally verify
their complementarity. Further, in spatial, we propose a Complementary Content
Attention (CCA) to take advantages of the coupled structure and guide
independent features for spatial complementary learning. In temporal, a
Hierarchical Temporal Aggregation (HTA) is proposed to progressively capture
the inter-frame dependencies and encode temporal information. Besides, a gated
attention is utilized to deliver aggregated temporal information into the CNN
and Transformer branches for temporal complementary learning. Finally, we
introduce a self-distillation training strategy to transfer the superior
spatial-temporal knowledge to backbone networks for higher accuracy and more
efficiency. In this way, two kinds of typical features from same videos are
integrated mechanically for more informative representations. Extensive
experiments on four public Re-ID benchmarks demonstrate that our framework
could attain better performances than most state-of-the-art methods.
