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People, Penguins and Petri Dishes: Adapting Object Counting Models to New Visual Domains and Object Types Without Forgetting
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
In this paper we propose a technique to adapt a convolutional neural network (CNN) based object counter to additional visual domains and object types while still preserving the original counting function. Domain-specific normalisation and scaling operators are trained to allow the model to adjust to the statistical distributions of the various visual domains. The developed adaptation technique is used to produce a singular patch-based counting regressor capable of counting various object types
doi:10.1109/cvpr.2018.00842
dblp:conf/cvpr/MarsdenMLKO18
fatcat:mc5rystcqzazlhm52opxqdwlky