  Vehicle re-identification helps in distinguishing between images of the same
and other vehicles. It is a challenging process because of significant
intra-instance differences between identical vehicles from different views and
subtle inter-instance differences between similar vehicles. To solve this
issue, researchers have extracted view-aware or part-specific features via
spatial attention mechanisms, which usually result in noisy attention maps or
otherwise require expensive additional annotation for metadata, such as key
points, to improve the quality. Meanwhile, based on the researchers' insights,
various handcrafted multi-attention architectures for specific viewpoints or
vehicle parts have been proposed. However, this approach does not guarantee
that the number and nature of attention branches will be optimal for real-world
re-identification tasks. To address these problems, we proposed a new vehicle
re-identification network based on a multiple soft attention mechanism for
capturing various discriminative regions from different viewpoints more
efficiently. Furthermore, this model can significantly reduce the noise in
spatial attention maps by devising a new method for creating an attention map
for insignificant regions and then excluding it from generating the final
result. We also combined a channel-wise attention mechanism with a spatial
attention mechanism for the efficient selection of important semantic
attributes for vehicle re-identification. Our experiments showed that our
proposed model achieved a state-of-the-art performance among the
attention-based methods without metadata and was comparable to the approaches
using metadata for the VehicleID and VERI-Wild datasets.
