Generalising without Forgetting for Lifelong Person Re-Identification

Authors

  • Guile Wu Queen Mary University of London
  • Shaogang Gong Queen Mary University of London

Keywords:

Image and Video Retrieval

Abstract

Existing person re-identification (Re-ID) methods mostly prepare all training data in advance, while real-world Re-ID data are inherently captured over time or from different locations, which requires a model to be incrementally generalised from sequential learning of piecemeal new data without forgetting what is already learned. In this work, we call this lifelong person Re-ID, characterised by solving a problem of unseen class identification subject to continuous new domain generalisation and adaptation with class imbalanced learning. We formulate a new Generalising without Forgetting method (GwFReID) for lifelong Re-ID and design a comprehensive learning objective that accounts for classification coherence, distribution coherence and representation coherence in a unified framework. This design helps to simultaneously learn new information, distil old knowledge and solve class imbalance, which enables GwFReID to incrementally improve model generalisation without catastrophic forgetting of what is already learned. Extensive experiments on eight Re-ID benchmarks, CIFAR-100 and ImageNet show the superiority of GwFReID over the state-of-the-art methods.

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Published

2021-05-18

How to Cite

Wu, G., & Gong, S. (2021). Generalising without Forgetting for Lifelong Person Re-Identification. Proceedings of the AAAI Conference on Artificial Intelligence, 35(4), 2889-2897. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16395

Issue

Section

AAAI Technical Track on Computer Vision III