Lifelong Person Re-Identification with Backward-Compatibility

arXiv:2403.10022v1 Announce Type: new
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 …

abstract arxiv catastrophic forgetting complexity computational cs.cv datasets however identification images person practical training training datasets type