Universal moving object segmentation aims to provide a general model for
videos from all types of natural scenes, as previous approaches are usually
effective for specific or similar scenes. In this paper, we propose a method
called Learning Temporal Distribution and Spatial Correlation (LTS) that has
the potential to be a general solution for universal moving object
segmentation. In the proposed approach, the distribution from temporal pixels
is first learned by our Defect Iterative Distribution Learning (DIDL) network
for a scene-independent segmentation. Then, the Stochastic Bayesian Refinement
(SBR) Network, which learns the spatial correlation, is proposed to improve the
binary mask generated by the DIDL network. Benefiting from the scene
independence of the temporal distribution and the accuracy improvement
resulting from the spatial correlation, the proposed approach performs well for
almost all videos from diverse and complex natural scenes with fixed
parameters. Comprehensive experiments on standard datasets including LASIESTA,
CDNet2014, BMC, SBMI2015 and 128 real world videos demonstrate the superiority
of proposed approach compared to state-of-the-art methods with or without the
use of deep learning networks. To the best of our knowledge, this work has high
potential to be a general solution for moving object segmentation in real world
environments.

本文提出了一种称为学习时空分布和空间相关性的方法，它的能量可以作为普适的解决方案来完成所有类型的自然场景下的运动目标分割，通过在分布学习中引入深度学习网络和在空间相关性中引入随机贝叶斯细化网络的方法，从而大大改善了分割的效果。

学习时间分布和空间相关性用于通用移动目标分割

Learning Temporal Distribution and Spatial Correlation for Universal  Moving Object Segmentation

Here, we propose a general method for probabilistic time series forecasting.
We combine an autoregressive recurrent neural network to model temporal
dynamics with Implicit Quantile Networks to learn a large class of
distributions over a time-series target. When compared to other probabilistic
neural forecasting models on real- and simulated data, our approach is
favorable in terms of point-wise prediction accuracy as well as on estimating
the underlying temporal distribution.

本文介绍了一种概率时间序列预测的一般方法，通过结合自回归循环神经网络和隐含量子网络来建模时间动态并学习一系列时间序列预测模型，在真实数据和模拟数据上得出的结果表明，该方法在预测准确性和估计潜在时间分布方面具有优势。