Abstract
Being a cross-camera retrieval task, person reidentification suffers from image style variations caused by
different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace.
In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation. CamStyle
can serve as a data augmentation approach that smooths
the camera style disparities. Specifically, with CycleGAN,
labeled training images can be style-transferred to each
camera, and, along with the original training samples,
form the augmented training set. This method, while increasing data diversity against over-fitting, also incurs a
considerable level of noise. In the effort to alleviate the
impact of noise, the label smooth regularization (LSR) is
adopted. The vanilla version of our method (without LSR)
performs reasonably well on few-camera systems in which
over-fitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of over-fitting. We also report competitive accuracy
compared with the state of the art. Code is available at:
https://github.com/zhunzhong07/CamStyle