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We consider the problem of few-shot scene adaptive crowd counting.
In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph.
For this purpose, a classifier evaluates the density level of the input features and then passes them to the corresponding high and low crowded DAD modules.
Crowd counting is a challenging problem especially in the presence of huge crowd diversity across images and complex cluttered crowd-like background regions, where most previous approaches do not generalize well and consequently produce either huge crowd underestimation or overestimation.
SOTA for Crowd Counting on ShanghaiTech
Counting the number of birds in an open sky setting has been an challenging problem due to the large number of bird flocks and the birds can overlap.
In this paper, we introduce a new variant of crowd counting problem, namely "Categorized Crowd Counting", that counts the number of people sitting and standing in a given image.
Then a coarse counter is trained on translated data and applied to the real world.
To reduce the gap, in this paper, we propose a domain-adaptation-style crowd counting method, which can effectively adapt the model from synthetic data to the specific real-world scenes.