We propose a unified representation learning framework to address the Cross
Model Compatibility (CMC) problem in the context of visual search applications.
Cross compatibility between different embedding models enables the visual
search systems to correctly recognize and retrieve identities without
re-encoding user images, which are usually not available due to privacy
concerns. While there are existing approaches to address CMC in face
identification, they fail to work in a more challenging setting where the
distributions of embedding models shift drastically. The proposed solution
improves CMC performance by introducing a light-weight Residual Bottleneck
Transformation (RBT) module and a new training scheme to optimize the embedding
spaces. Extensive experiments demonstrate that our proposed solution
outperforms previous approaches by a large margin for various challenging
visual search scenarios of face recognition and person re-identification.

本研究提出一种统一的表示学习框架，解决了在视觉搜索应用中交叉模型兼容性（CMC）问题，通过引入轻量级的 RBT 模块和新的训练方案来优化嵌入空间，该方案在各种具有挑战性的视觉搜索情景下，包括面部识别和个人重新识别，均优于先前的方法。