  Vehicle re-identification is a cross-view search task by matching the same
target vehicle from different perspectives. It serves an important role in
road-vehicle collaboration and intelligent road control. With the large-scale
and dynamic road environment, the paradigm of supervised vehicle
re-identification shows limited scalability because of the heavy reliance on
large-scale annotated datasets. Therefore, the unsupervised vehicle
re-identification with stronger cross-scene generalization ability has
attracted more attention. Considering that Masked Autoencoder (MAE) has shown
excellent performance in self-supervised learning, this work designs a Contour
Guided Masked Autoencoder for Unsupervised Vehicle Re-Identification (ConMAE),
which is inspired by extracting the informative contour clue to highlight the
key regions for cross-view correlation. ConMAE is implemented by preserving the
image blocks with contour pixels and randomly masking the blocks with smooth
textures. In addition, to improve the quality of pseudo labels of vehicles for
unsupervised re-identification, we design a label softening strategy and
adaptively update the label with the increase of training steps. We carry out
experiments on VeRi-776 and VehicleID datasets, and a significant performance
improvement is obtained by the comparison with the state-of-the-art
unsupervised vehicle re-identification methods. The code is available on the
website of https://github.com/2020132075/ConMAE.
