  The task of hot-refresh model upgrades of image retrieval systems plays an
essential role in the industry but has never been investigated in academia
before. Conventional cold-refresh model upgrades can only deploy new models
after the gallery is overall backfilled, taking weeks or even months for
massive data. In contrast, hot-refresh model upgrades deploy the new model
immediately and then gradually improve the retrieval accuracy by backfilling
the gallery on-the-fly. Compatible training has made it possible, however, the
problem of model regression with negative flips poses a great challenge to the
stable improvement of user experience. We argue that it is mainly due to the
fact that new-to-old positive query-gallery pairs may show less similarity than
new-to-new negative pairs. To solve the problem, we introduce a
Regression-Alleviating Compatible Training (RACT) method to properly constrain
the feature compatibility while reducing negative flips. The core is to
encourage the new-to-old positive pairs to be more similar than both the
new-to-old negative pairs and the new-to-new negative pairs. An efficient
uncertainty-based backfilling strategy is further introduced to fasten accuracy
improvements. Extensive experiments on large-scale retrieval benchmarks (e.g.,
Google Landmark) demonstrate that our RACT effectively alleviates the model
regression for one more step towards seamless model upgrades. The code will be
available at https://github.com/binjiezhang/RACT_ICLR2022.
