  Deep convolutional neural networks (CNNs) have demonstrated dominant
performance in person re-identification (Re-ID). Existing CNN based methods
utilize global average pooling (GAP) to aggregate intermediate convolutional
features for Re-ID. However, this strategy only considers the first-order
statistics of local features and treats local features at different locations
equally important, leading to sub-optimal feature representation. To deal with
these issues, we propose a novel weighted bilinear coding (WBC) framework for
local feature aggregation in CNN networks to pursue more representative and
discriminative feature representations, which can adapt to other
state-of-the-art methods and improve their performance. In specific, bilinear
coding is used to encode the channel-wise feature correlations to capture
richer feature interactions. Meanwhile, a weighting scheme is applied on the
bilinear coding to adaptively adjust the weights of local features at different
locations based on their importance in recognition, further improving the
discriminability of feature aggregation. To handle the spatial misalignment
issue, we use a salient part net (spatial attention module) to derive salient
body parts, and apply the WBC model on each part. The final representation,
formed by concatenating the WBC encoded features of each part, is both
discriminative and resistant to spatial misalignment. Experiments on three
benchmarks including Market-1501, DukeMTMC-reID and CUHK03 evidence the
favorable performance of our method against other outstanding methods.
