  This paper proposes a self-supervised learning method for the person
re-identification (re-ID) problem, where existing unsupervised methods usually
rely on pseudo labels, such as those from video tracklets or clustering. A
potential drawback of using pseudo labels is that errors may accumulate and it
is challenging to estimate the number of pseudo IDs. We introduce a different
unsupervised method that allows us to learn pedestrian embeddings from raw
videos, without resorting to pseudo labels. The goal is to construct a
self-supervised pretext task that matches the person re-ID objective. Inspired
by the \emph{data association} concept in multi-object tracking, we propose the
\textbf{Cyc}le \textbf{As}sociation (\textbf{CycAs}) task: after performing
data association between a pair of video frames forward and then backward, a
pedestrian instance is supposed to be associated to itself. To fulfill this
goal, the model must learn a meaningful representation that can well describe
correspondences between instances in frame pairs. We adapt the discrete
association process to a differentiable form, such that end-to-end training
becomes feasible. Experiments are conducted in two aspects: We first compare
our method with existing unsupervised re-ID methods on seven benchmarks and
demonstrate CycAs' superiority. Then, to further validate the practical value
of CycAs in real-world applications, we perform training on self-collected
videos and report promising performance on standard test sets.
