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Adaptive Dilated Network With Self-Correction Supervision for Counting
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
The counting problem aims to estimate the number of objects in images. Due to large scale variation and labeling deviations, it remains a challenging task. The static density map supervised learning framework is widely used in existing methods, which uses the Gaussian kernel to generate a density map as the learning target and utilizes the Euclidean distance to optimize the model. However, the framework is intolerable to the labeling deviations and can not reflect the scale variation. In this
doi:10.1109/cvpr42600.2020.00465
dblp:conf/cvpr/BaiHQHWY20
fatcat:ygf34vaqjfhz3axdbsmp52us34