Person re-identification (Re-ID) benefits greatly from the accurate annotations of existing datasets (e.g., CUHK03 [1] and Market-1501 [2]), which are quite expensive because each image in these datasets has to be assigned with a proper label. In this work, we ease the annotation of Re-ID by replacing the accurate annotation with inaccurate annotation, i.e., we group the images into bags in terms of time and assign a bag-level label for each bag. This greatly reduces the annotation effort and leads to the creation of a large-scale Re-ID benchmark called SYSU-30k. The new benchmark contains 30k individuals, which is about 20 times larger than CUHK03 (1.3k individuals) and Market-1501 (1.5k individuals), and 30 times larger than ImageNet (1k categories). It sums up to 29,606,918 images. Learning a Re-ID model with bag-level annotation is called the weakly supervised Re-ID problem. To solve this problem, we introduce a differentiable graphical model to capture the dependencies from all images in a bag and generate a reliable pseudo label for each person image. The pseudo label is further used to supervise the learning of the Re-ID model. When compared with the fully supervised Re-ID models, our method achieves state-of-the-art performance on SYSU-30k and other datasets. The code, dataset, and pretrained model will be available at \url{https://github.com/wanggrun/SYSU-30k}.
@article{arxiv.1904.03845,
title = {Weakly Supervised Person Re-ID: Differentiable Graphical Learning and A New Benchmark},
author = {Guangrun Wang and Guangcong Wang and Xujie Zhang and Jianhuang Lai and Zhengtao Yu and Liang Lin},
journal= {arXiv preprint arXiv:1904.03845},
year = {2020}
}