  Since neural networks are data-hungry, incorporating data augmentation in
training is a widely adopted technique that enlarges datasets and improves
generalization. On the other hand, aggregating predictions of multiple
augmented samples (i.e., test-time augmentation) could boost performance even
further. In the context of person re-identification models, it is common
practice to extract embeddings for both the original images and their
horizontally flipped variants. The final representation is the mean of the
aforementioned feature vectors. However, such scheme results in a gap between
training and inference, i.e., the mean feature vectors calculated in inference
are not part of the training pipeline. In this study, we devise the FlipReID
structure with the flipping loss to address this issue. More specifically,
models using the FlipReID structure are trained on the original images and the
flipped images simultaneously, and incorporating the flipping loss minimizes
the mean squared error between feature vectors of corresponding image pairs.
Extensive experiments show that our method brings consistent improvements. In
particular, we set a new record for MSMT17 which is the largest person
re-identification dataset. The source code is available at
https://github.com/nixingyang/FlipReID.
