  Unsupervised crowd counting is a challenging yet not largely explored task.
In this paper, we explore it in a transfer learning setting where we learn to
detect and count persons in an unlabeled target set by transferring
bi-knowledge learnt from regression- and detection-based models in a labeled
source set. The dual source knowledge of the two models is heterogeneous and
complementary as they capture different modalities of the crowd distribution.
We formulate the mutual transformations between the outputs of regression- and
detection-based models as two scene-agnostic transformers which enable
knowledge distillation between the two models. Given the regression- and
detection-based models and their mutual transformers learnt in the source, we
introduce an iterative self-supervised learning scheme with
regression-detection bi-knowledge transfer in the target. Extensive experiments
on standard crowd counting benchmarks, ShanghaiTech, UCF\_CC\_50, and UCF\_QNRF
demonstrate a substantial improvement of our method over other
state-of-the-arts in the transfer learning setting.
