Recently, segmentation-based scene text detection methods have drawn
extensive attention in the scene text detection field, because of their
superiority in detecting the text instances of arbitrary shapes and extreme
aspect ratios, profiting from the pixel-level descriptions. However, the vast
majority of the existing segmentation-based approaches are limited to their
complex post-processing algorithms and the scale robustness of their
segmentation models, where the post-processing algorithms are not only isolated
to the model optimization but also time-consuming and the scale robustness is
usually strengthened by fusing multi-scale feature maps directly. In this
paper, we propose a Differentiable Binarization (DB) module that integrates the
binarization process, one of the most important steps in the post-processing
procedure, into a segmentation network. Optimized along with the proposed DB
module, the segmentation network can produce more accurate results, which
enhances the accuracy of text detection with a simple pipeline. Furthermore, an
efficient Adaptive Scale Fusion (ASF) module is proposed to improve the scale
robustness by fusing features of different scales adaptively. By incorporating
the proposed DB and ASF with the segmentation network, our proposed scene text
detector consistently achieves state-of-the-art results, in terms of both
detection accuracy and speed, on five standard benchmarks.

该论文提出了一种基于分割的场景文本检测方法，结合差分二值化和自适应尺度融合模块，显著提升了文本检测的准确度和速度。

不可微二值化与自适应尺度融合的实时场景文本检测

Real-Time Scene Text Detection with Differentiable Binarization and  Adaptive Scale Fusion

Lane mark detection is an important element in the road scene analysis for
Advanced Driver Assistant System (ADAS). Limited by the onboard computing
power, it is still a challenge to reduce system complexity and maintain high
accuracy at the same time. In this paper, we propose a Lane Marking Detector
(LMD) using a deep convolutional neural network to extract robust lane marking
features. To improve its performance with a target of lower complexity, the
dilated convolution is adopted. A shallower and thinner structure is designed
to decrease the computational cost. Moreover, we also design post-processing
algorithms to construct 3rd-order polynomial models to fit into the curved
lanes. Our system shows promising results on the captured road scenes.

本文提出了一种使用深度卷积神经网络进行车道标记检测的算法，采用扩张卷积、精简模型结构等手段提高其低复杂度、高准确性，再采用后处理算法对曲线车道建模，该算法在拍摄到的路况场景下表现优异。

利用深度学习技术实现高效道路车道标线检测

Efficient Road Lane Marking Detection with Deep Learning

Recent works in recommendation systems have focused on diversity in
recommendations as an important aspect of recommendation quality. In this work
we argue that the post-processing algorithms aimed at only improving diversity
among recommendations lead to discrimination among the users. We introduce the
notion of user fairness which has been overlooked in literature so far and
propose measures to quantify it. Our experiments on two diversification
algorithms show that an increase in aggregate diversity results in increased
disparity among the users.

本文提出并探讨了用户公平性的概念，研究了两个多样化算法，发现仅追求多样性的推荐会导致用户间的不公平现象。