Lifelong person re-identification (LReID) assumes a practical scenario where
the model is sequentially trained on continuously incoming datasets while
alleviating the catastrophic forgetting in the old datasets. However, not only
the training datasets but also the gallery images are incrementally
accumulated, that requires a huge amount of computational complexity and
storage space to extract the features at the inference phase. In this paper, we
address the above mentioned problem by incorporating the backward-compatibility
to LReID for the first time. We train the model using the continuously incoming
datasets while maintaining the model's compatibility toward the previously
trained old models without re-computing the features of the old gallery images.
To this end, we devise the cross-model compatibility loss based on the
contrastive learning with respect to the replay features across all the old
datasets. Moreover, we also develop the knowledge consolidation method based on
the part classification to learn the shared representation across different
datasets for the backward-compatibility. We suggest a more practical
methodology for performance evaluation as well where all the gallery and query
images are considered together. Experimental results demonstrate that the
proposed method achieves a significantly higher performance of the
backward-compatibility compared with the existing methods. It is a promising
tool for more practical scenarios of LReID.

我们提出了具备向后兼容性的一种连续学习人员重识别模型，通过引入跨模型兼容性损失和知识整合方法，实现在连续到来的数据集上训练模型时对先前训练的旧模型的向后兼容性。实验结果表明，与现有方法相比，所提出的方法在向后兼容性方面取得了显着提高的性能，是更实际的人员重识别领域的有希望的工具。