Dynamic Mixture of Counter Network for Location-Agnostic Crowd Counting

Crowd counting has attracted increasing attentions in recent years due to its challenges and wide societal applications. Despite persevering efforts made by the research community, most of existing methods require a large amount of location-level annotations. Collecting such type of fine-granularity supervisory signals is extremely time-consuming and labour-intensive, thereby hindering the well generalization of these location-adherent models. To shun this drawback, several pioneering studies open a promising research direction of location-agonistic crowd counting. Albeit the noticeable efforts, they somewhat ignore the merits of diverse learning paradigms and the issue of intractable density shift. To ameliorate these issues, in this paper, a novel Dynamic Mixture of Counter Network (DMCNet) is proposed for location-agnostic crowd counting. Specifically, our DMCNet inherits the hybrid advantages of CNNs (e.g. locality-oriented and pyramidal property) and MLP-based structure (e.g. global receptive fields and light weight). Particularly, the dynamic counter predictor and the mixture of counter heads are delicately designed to hammer at combating huge density shift and overfitting. Extensive experiments demonstrate that our DMCNet attains state-of-the-art performance against existing location-agnostic approaches and performs on par with many conventional location-adherent ones.