Backfilling is the process of re-extracting all gallery embeddings from upgraded models in image retrieval systems. It inevitably requires a prohibitively large amount of computational cost and even entails the downtime of the service. Although backward-compatible learning sidesteps this challenge by tackling query-side representations, this leads to suboptimal solutions in principle because gallery embeddings cannot benefit from model upgrades. We address this dilemma by introducing an online backfilling algorithm, which enables us to achieve a progressive performance improvement during the backfilling process while not sacrificing the final performance of new model after the completion of backfilling. To this end, we first propose a simple distance rank merge technique for online backfilling. Then, we incorporate a reverse transformation module for more effective and efficient merging, which is further enhanced by adopting a metric-compatible contrastive learning approach. These two components help to make the distances of old and new models compatible, resulting in desirable merge results during backfilling with no extra computational overhead. Extensive experiments show the effectiveness of our framework on four standard benchmarks in various settings.

翻译：回填是指在图像检索系统中，从升级后的模型中重新提取所有图库嵌入的过程。这一过程不可避免地需要极高的计算成本，甚至可能导致服务中断。尽管后向兼容学习通过处理查询端表征来规避这一挑战，但这在原则上会导致次优解，因为图库嵌入无法从模型升级中获益。我们通过引入一种在线回填算法来解决这一困境，该算法使我们能够在回填过程中实现渐进式的性能提升，同时不牺牲回填完成后新模型的最终性能。为此，我们首先提出了一种用于在线回填的简单距离排序融合技术。接着，我们引入了一个反向转换模块以实现更高效、更有效的融合，并通过采用度量兼容的对比学习方法进一步增强。这两个组件有助于使新旧模型的距离度量兼容，从而在回填过程中实现理想的融合结果，且无需额外的计算开销。大量实验表明，我们的框架在多种设置下的四个标准基准测试中均表现出有效性。