Single-stage object detectors have been widely applied in computer vision applications due to their high efficiency. However, the loss functions adopted by single-stage hurt localization accuracy seriously. Firstly, cross-entropy for classification is independent of task and drives all positive examples learn as scores possible regardless accuracy. Thus, there exist many detections with but low...

We consider the problem of estimating joint distribution n independent random variables. Given a loss function and family candidate probabilities, that we shall call model, aim at designing an estimator with values in our model possesses good estimation properties not only when data belongs to but also it lies close enough it. The losses have mind are total variation, Hellinger, Wasserstein $${...

In this paper, the estimation of a scale parameter t under a new and bounded loss function, based on a reflection of the gamma density function, is discussed. The best scale-invariant estimator of tis obtained and the admissibility of all linear functions of the sufficient statistic, for estimating t in the absence of a nuisance parameter, is investigated