Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification
kai wang (Computer Vision Center),* Chenshen Wu (Computer Vision Center), Andy Bagdanov (University of Florence, Italy), Xialei Liu (Nankai University), Shiqi Yang (Computer Vision Center), Shangling Jui (Huawei Kirin Solution), Joost van de Weijer (Computer Vision Center)
The 33rd British Machine Vision Conference
Abstract
Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and that generalizes to previously unseen re-identification tasks. The main challenge is that at inference time the representation must generalize to previously unseen identities. To address this problem, we apply continual meta metric learning to lifelong object re-identification. To prevent forgetting of previous tasks, we use knowledge distillation and explore the roles of positive and negative pairs. Based on our observation that the distillation and metric losses are antagonistic, we propose to remove positive pairs from distillation to robustify model updates. Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on extensive intra-domain experiments on person and vehicle re-identification datasets, as well as inter-domain experiments on the LReID benchmark. Our experiments demonstrate that DwoPP significantly outperforms the state-of-the-art.Video
Citation
@inproceedings{wang_2022_BMVC,
author = {kai wang and Chenshen Wu and Andy Bagdanov and Xialei Liu and Shiqi Yang and Shangling Jui and Joost van de Weijer},
title = {Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0038.pdf}
}
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