A novel ordinal regression algorithm, called moving window regression (MWR),
is proposed in this paper. First, we propose the notion of relative rank
($\rho$-rank), which is a new order representation scheme for input and
reference instances. Second, we develop global and local relative regressors
($\rho$-regressors) to predict $\rho$-ranks within entire and specific rank
ranges, respectively. Third, we refine an initial rank estimate iteratively by
selecting two reference instances to form a search window and then estimating
the $\rho$-rank within the window. Extensive experiments results show that the
proposed algorithm achieves the state-of-the-art performances on various
benchmark datasets for facial age estimation and historical color image
classification. The codes are available at this https URL

本文提出了一种新的序数回归算法 —— 移动窗口回归 (MWR)。在算法中，我们设计了基于相对排序的表示模式、全局与局部相对回归模型以及迭代式的排名估计方法。实验结果表明，该算法在面部年龄估计和彩色图像分类等多个数据集上均取得了最先进的表现。

移动窗口回归：一种新的序回归方法

Moving Window Regression: A Novel Approach to Ordinal Regression

Salient object detection is a problem that has been considered in detail and
many solutions proposed. In this paper, we argue that work to date has
addressed a problem that is relatively ill-posed. Specifically, there is not
universal agreement about what constitutes a salient object when multiple
observers are queried. This implies that some objects are more likely to be
judged salient than others, and implies a relative rank exists on salient
objects. The solution presented in this paper solves this more general problem
that considers relative rank, and we propose data and metrics suitable to
measuring success in a relative object saliency landscape. A novel deep
learning solution is proposed based on a hierarchical representation of
relative saliency and stage-wise refinement. We also show that the problem of
salient object subitizing can be addressed with the same network, and our
approach exceeds performance of any prior work across all metrics considered
(both traditional and newly proposed).

本文提出了一种基于深度学习和分层表示的相对显著性检测方法，解决了在多个观察者查询时显著对象定义模糊的问题，同时还能够用于解决显著性目标子计数问题，并在所有指标上的性能优于先前的任何工作。