Abstract
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.
Abstract (translated)
终身人物识别（LReID）假定一个实际场景，即在连续的 incoming 数据集中对模型进行序列训练，同时减轻 old 数据集中的灾难性遗忘。然而，不仅训练数据集，还包括画廊图像，都需要积累大量的计算复杂度和存储空间，在推理阶段提取特征。在本文中，我们通过首次将反向兼容性引入 LReID，解决了上述提到的这个问题。我们在连续的 incoming 数据集上训练模型，同时保持模型对之前训练的旧模型的兼容性，而不重新计算旧画廊图像的特征。为此，我们根据所有 old 数据集的对比学习，设计了一种跨模态兼容性损失。此外，我们还基于部分分类开发了知识整合方法，以学习不同数据集之间的共享表示。我们建议一种更实际的性能评估方法，其中所有画廊和查询图像都被考虑在内。实验结果表明，与现有方法相比，所提出的方法在反向兼容性方面取得了显著的提高。这是一个有前景的工具，适用于更实际的 LReID 场景。
URL
https://arxiv.org/abs/2403.10022
