Image segmentation is an important step in most visual tasks. While
convolutional neural networks have shown to perform well on single image
segmentation, to our knowledge, no study has been been done on leveraging
recurrent gated architectures for video segmentation. Accordingly, we propose a
novel method for online segmentation of video sequences that incorporates
temporal data. The network is built from fully convolutional element and
recurrent unit that works on a sliding window over the temporal data. We also
introduce a novel convolutional gated recurrent unit that preserves the spatial
information and reduces the parameters learned. Our method has the advantage
that it can work in an online fashion instead of operating over the whole input
batch of video frames. The network is tested on the change detection dataset,
and proved to have 5.5\% improvement in F-measure over a plain fully
convolutional network for per frame segmentation. It was also shown to have
improvement of 1.4\% for the F-measure compared to our baseline network that we
call FCN 12s.

本文提出了基于卷积门控循环单元网络的在线视频分割方法，利用滑动窗口对时间数据进行处理，并在变化检测数据集上进行实验，相比于常规的全卷积神经网络有 5.5% 的性能提升。

基于全卷积递归网络的视频分割

Recurrent Fully Convolutional Networks for Video Segmentation

Our aim is to investigate ontology-based data access over temporal data with
validity time and ontologies capable of temporal conceptual modelling. To this
end, we design a temporal description logic, TQL, that extends the standard
ontology language OWL 2 QL, provides basic means for temporal conceptual
modelling and ensures first-order rewritability of conjunctive queries for
suitably defined data instances with validity time.

研究本文探讨了基于本体的数据访问和时间概念建模，并设计了一种扩展 OWL 2 QL 标准本体语言的 TQL 的时间描述逻辑，以提供基本的时间概念建模工具，并确保对于数据实例的适当定义，具有一阶可重写的连接查询。