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.

翻译：在目标再识别(ReID)中,深层次学习技术的开发往往涉及模式更新和部署。在部署新模型时,重装和重新索引与系统中止是难以忍受的。因此,提议后向兼容的表达方式是为了能够将“新”特征与“旧”特征直接进行比较,这意味着数据库在“新”和“旧”特征同时存在时即活跃。因此,我们可以滚动更新数据库,甚至数据库上没有任何内容更新。现有的后向兼容方法要么要求老和新培训数据之间有很强的重叠,要么仅仅在实例一级进行限制。因此,它们难以处理复杂的集群结构,在消除旧嵌入中的外部效应方面受到限制,从而有可能损害新特征的歧视性能力。在这项工作中,我们提出了“近邻共识对比学习”方法。在不假定新培训数据模型的情况下,我们估计了旧嵌入的子集群结构。新的嵌入方式受到制约,因为在嵌入空间再嵌入的精度方面有许多老旧的嵌入,因此,我们更精确性也能够利用旧的方法来改进后层系统。我们更精确的校制方法,我们更精确地选择了旧的校制方法。