  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-30$k$. 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-30$k$ and other datasets.
The code, dataset, and pretrained model will be available at
\url{https://github.com/wanggrun/SYSU-30k}.

Computer Vision and Pattern Recognition;Artificial Intelligence