  Crowd localization targets on predicting each instance precise location
within an image. Current advanced methods propose the pixel-wise binary
classification to tackle the congested prediction, in which the pixel-level
thresholds binarize the prediction confidence of being the pedestrian head.
Since the crowd scenes suffer from extremely varying contents, counts and
scales, the confidence-threshold learner is fragile and under-generalized
encountering domain knowledge shift. Moreover, at the most time, the target
domain is agnostic in training. Hence, it is imperative to exploit how to
enhance the generalization of confidence-threshold locator to the latent target
domain. In this paper, we propose a Dynamic Proxy Domain (DPD) method to
generalize the learner under domain shift. Concretely, based on the theoretical
analysis to the generalization error risk upper bound on the latent target
domain to a binary classifier, we propose to introduce a generated proxy domain
to facilitate generalization. Then, based on the theory, we design a DPD
algorithm which is composed by a training paradigm and proxy domain generator
to enhance the domain generalization of the confidence-threshold learner.
Besides, we conduct our method on five kinds of domain shift scenarios,
demonstrating the effectiveness on generalizing the crowd localization. Our
code will be available at https://github.com/zhangda1018/DPD.
