  In object re-identification (ReID), the development of deep learning
techniques often involves model updates and deployment. It is unbearable to
re-embedding and re-index with the system suspended when deploying new models.
Therefore, backward-compatible representation is proposed to enable "new"
features to be compared with "old" features directly, which means that the
database is active when there are both "new" and "old" features in it. Thus we
can scroll-refresh the database or even do nothing on the database to update.
  The existing backward-compatible methods either require a strong overlap
between old and new training data or simply conduct constraints at the instance
level. Thus they are difficult in handling complicated cluster structures and
are limited in eliminating the impact of outliers in old embeddings, resulting
in a risk of damaging the discriminative capability of new features. In this
work, we propose a Neighborhood Consensus Contrastive Learning (NCCL) method.
With no assumptions about the new training data, we estimate the sub-cluster
structures of old embeddings. A new embedding is constrained with multiple old
embeddings in both embedding space and discrimination space at the sub-class
level. The effect of outliers diminished, as the multiple samples serve as
"mean teachers". Besides, we also propose a scheme to filter the old embeddings
with low credibility, further improving the compatibility robustness. Our
method ensures backward compatibility without impairing the accuracy of the new
model. And it can even improve the new model's accuracy in most scenarios.
