  Recognizing a target of interest from the UAVs is much more challenging than
the existing object re-identification tasks across multiple city cameras. The
images taken by the UAVs usually suffer from significant size difference when
generating the object bounding boxes and uncertain rotation variations.
Existing methods are usually designed for city cameras, incapable of handing
the rotation issue in UAV scenarios. A straightforward solution is to perform
the image-level rotation augmentation, but it would cause loss of useful
information when inputting the powerful vision transformer as patches. This
motivates us to simulate the rotation operation at the patch feature level,
proposing a novel rotation invariant vision transformer (RotTrans). This
strategy builds on high-level features with the help of the specificity of the
vision transformer structure, which enhances the robustness against large
rotation differences. In addition, we design invariance constraint to establish
the relationship between the original feature and the rotated features,
achieving stronger rotation invariance. Our proposed transformer tested on the
latest UAV datasets greatly outperforms the current state-of-the-arts, which is
5.9\% and 4.8\% higher than the highest mAP and Rank1. Notably, our model also
performs competitively for the person re-identification task on traditional
city cameras. In particular, our solution wins the first place in the UAV-based
person re-recognition track in the Multi-Modal Video Reasoning and Analyzing
Competition held in ICCV 2021. Code is available at
https://github.com/whucsy/RotTrans.
