Patch-based Knowledge Distillation for Lifelong Person Re-Identification

By Zhicheng Sun, Yadong Mu. 2022

45 citations recorded by the source corpus.

The task of lifelong person re-identification aims to match a person across multiple cameras given continuous data streams. Similar to other lifelong learning tasks, it severely suffers from the so-called catastrophic forgetting problem, which refers to the notable performance degradation on previously-seen data after adapting the model to some newly incoming data. To alleviate it, a few existing methods have utilized knowledge distillation to enforce consistency between the original and adapted models. However, the effectiveness of such a strategy can be largely reduced facing the data distribution discrepancy between seen and new data. The hallmark of our work is using adaptively-chosen patches (rather than whole images as in other works) to pilot the forgetting-resistant distillation. Specifically, the technical contributions of our patch-based new solution are two-fold: first, a novel patch sampler is proposed. It is fully differentiable and trained to select a diverse set of image patches that stay crucial and discriminative under streaming data. Secondly, with those patches we curate a novel knowledge distillation framework. Valuable patch-level knowledge within individual patch features and mutual relations is well preserved by the two newly introduced distillation modules, further mitigating catastrophic forgetting. Extensive experiments on twelve person re-identification datasets clearly validate the superiority of our method over state-of-the-art competitors by large performance margins.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jiahuan Zhou
2. Yuxin Peng
3. Zhenyu Cui
4. Xun Wang
5. Manyu Zhu

In Computer Vision and Pattern Recognition

1. Jiahuan Zhou
2. Yuxin Peng
3. Zhenyu Cui
4. Xun Wang
5. Manyu Zhu

Citations in the corpus

• Learning Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification (2024, methodology): “Following [27], each dataset is randomly sampled with 500 identities to alleviate the problem of unbalanced class numbers among different datasets for fairness comparison.”