  Despite various methods are proposed to make progress in pedestrian attribute
recognition, a crucial problem on existing datasets is often neglected, namely,
a large number of identical pedestrian identities in train and test set, which
is not consistent with practical application. Thus, images of the same
pedestrian identity in train set and test set are extremely similar, leading to
overestimated performance of state-of-the-art methods on existing datasets. To
address this problem, we propose two realistic datasets
PETA\textsubscript{$zs$} and RAPv2\textsubscript{$zs$} following zero-shot
setting of pedestrian identities based on PETA and RAPv2 datasets. Furthermore,
compared to our strong baseline method, we have observed that recent
state-of-the-art methods can not make performance improvement on PETA, RAPv2,
PETA\textsubscript{$zs$} and RAPv2\textsubscript{$zs$}. Thus, through solving
the inherent attribute imbalance in pedestrian attribute recognition, an
efficient method is proposed to further improve the performance. Experiments on
existing and proposed datasets verify the superiority of our method by
achieving state-of-the-art performance.
