【文件属性】:
文件名称:Focal Loss for Dense Object Detection
文件大小:1.12MB
文件格式:PDF
更新时间:2021-08-23 08:48:11
何凯明
The highest accuracy object detectors to date are based
on a two-stage approach popularized by R-CNN, where a
classifier is applied to a sparse set of candidate object locations.
In contrast, one-stage detectors that are applied
over a regular, dense sampling of possible object locations
have the potential to be faster and simpler, but have trailed
the accuracy of two-stage detectors thus far. In this paper,
we investigate why this is the case. We discover that the extreme
foreground-background class imbalance encountered
during training of dense detectors is the central cause. We
propose to address this class imbalance by reshaping the
standard cross entropy loss such that it down-weights the
loss assigned to well-classified examples.