The exponential growth of data, alongside advancements in model structures and loss functions, has necessitated the enhancement of image retrieval systems through the utilization of new models with superior feature embeddings. However, the expensive process of updating the old retrieval database by replacing embeddings poses a challenge. As a solution, backward-compatible training can be employed to avoid the necessity of updating old retrieval datasets. While previous methods achieved backward compatibility by aligning prototypes of the old model, they often overlooked the distribution of the old features, thus limiting their effectiveness when the old model's low quality leads to a weakly discriminative feature distribution. On the other hand, instance-based methods like L2 regression take into account the distribution of old features but impose strong constraints on the performance of the new model itself. In this paper, we propose MixBCT, a simple yet highly effective backward-compatible training method that serves as a unified framework for old models of varying qualities. Specifically, we summarize four constraints that are essential for ensuring backward compatibility in an ideal scenario, and we construct a single loss function to facilitate backward-compatible training. Our approach adaptively adjusts the constraint domain for new features based on the distribution of the old embeddings. We conducted extensive experiments on the large-scale face recognition datasets MS1Mv3 and IJB-C to verify the effectiveness of our method. The experimental results clearly demonstrate its superiority over previous methods. Code is available at https://github.com/yuleung/MixBCT

翻译：数据呈指数级增长，伴随着模型结构与损失函数的持续改进，促使图像检索系统需通过采用具备更优特征嵌入的新模型来提升性能。然而，更新旧检索数据库中嵌入向量的过程代价高昂，这成为一项挑战。为此，向后兼容训练可作为解决方案，从而避免更新旧检索数据集的必要性。现有方法虽通过对齐旧模型的原型实现向后兼容，但往往忽略旧特征的分布特性，当旧模型质量较低导致其特征分布鉴别性不足时，其有效性会受限。另一方面，基于实例的方法（如L2回归）虽能考虑旧特征分布，却会对新模型自身的性能施加过强约束。本文提出MixBCT——一种简单而高效的向后兼容训练方法，可作为不同质量旧模型的统一框架。具体而言，我们归纳了理想场景下确保向后兼容所需的四个约束，并构建单一损失函数以促进向后兼容训练。该方法能根据旧嵌入向量的分布特性自适应调整新特征的约束域。我们在大规模人脸识别数据集MS1Mv3与IJB-C上开展大量实验验证方法有效性，实验结果明确表明其性能优于现有方法。代码开源于 https://github.com/yuleung/MixBCT