Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning

Visual retrieval system faces frequent model update and deployment. It is a heavy workload to re-extract features of the whole database every time. Feature compatibility enables the learned new visual features to be directly compared with the old features stored in the database. In this way, when updating the deployed model, we can bypass the inflexible and time-consuming feature re-extraction process. However, the old feature space that needs to be compatible is not ideal and faces outlier samples. Besides, the new and old models may be supervised by different losses, which will further causes distribution discrepancy problem between these two feature spaces. In this work, we propose a global optimization Dual-Tuning method to obtain feature compatibility against different networks and losses. A feature-level prototype loss is proposed to explicitly align two types of embedding features, by transferring global prototype information. Furthermore, we design a component-level mutual structural regularization to implicitly optimize the feature intrinsic structure. Experiments are conducted on six datasets, including person ReID datasets, face recognition datasets, and million-scale ImageNet and Place365. Experimental results demonstrate that our Dual-Tuning is able to obtain feature compatibility without sacrificing performance.