Abstract
This paper addresses the variation generalized feature learning problem in unsupervised video-based
person re-identification (re-ID). With advanced
tracking and detection algorithms, large-scale intraview positive samples can be easily collected by
assuming that the image frames within the tracking sequence belong to the same person. Existing
methods either directly use the intra-view positives
to model cross-view variations or simply minimize
the intra-view variations to capture the invariant
component with some discriminative information
loss. In this paper, we propose a Variation Generalized Feature Learning (VGFL) method to learn
adaptable feature representation with intra-view
positives. The proposed method can learn a discriminative re-ID model without any manually annotated cross-view positive sample pairs. It could
address the unseen testing variations with a novel variation generalized feature learning algorithm. In addition, an Adaptability-Discriminability
(AD) fusion method is introduced to learn adaptable video-level features. Extensive experiments on
different datasets demonstrate the effectiveness of
the proposed method.