  Recent deep networks have convincingly demonstrated high capability in crowd
counting, which is a critical task attracting widespread attention due to its
various industrial applications. Despite such progress, trained data-dependent
models usually can not generalize well to unseen scenarios because of the
inherent domain shift. To facilitate this issue, this paper proposes a novel
adversarial scoring network (ASNet) to gradually bridge the gap across domains
from coarse to fine granularity. In specific, at the coarse-grained stage, we
design a dual-discriminator strategy to adapt source domain to be close to the
targets from the perspectives of both global and local feature space via
adversarial learning. The distributions between two domains can thus be aligned
roughly. At the fine-grained stage, we explore the transferability of source
characteristics by scoring how similar the source samples are to target ones
from multiple levels based on generative probability derived from coarse stage.
Guided by these hierarchical scores, the transferable source features are
properly selected to enhance the knowledge transfer during the adaptation
process. With the coarse-to-fine design, the generalization bottleneck induced
from the domain discrepancy can be effectively alleviated. Three sets of
migration experiments show that the proposed methods achieve state-of-the-art
counting performance compared with major unsupervised methods.
