We propose a normalization layer for unsupervised domain adaption in semantic
scene segmentation. Normalization layers are known to improve convergence and
generalization and are part of many state-of-the-art fully-convolutional neural
networks. We show that conventional normalization layers worsen the performance
of current Unsupervised Adversarial Domain Adaption (UADA), which is a method
to improve network performance on unlabeled datasets and the focus of our
research. Therefore, we propose a novel Domain Agnostic Normalization layer and
thereby unlock the benefits of normalization layers for unsupervised
adversarial domain adaptation. In our evaluation, we adapt from the synthetic
GTA5 data set to the real Cityscapes data set, a common benchmark experiment,
and surpass the state-of-the-art. As our normalization layer is domain agnostic
at test time, we furthermore demonstrate that UADA using Domain Agnostic
Normalization improves performance on unseen domains, specifically on
Apolloscape and Mapillary.

我们提出了一种适用于语义场景分割领域的无监督领域自适应规范化层，通过引入 Domain Agnostic Normalization 层，我们的方法在 GTA5 和 Cityscapes 数据集上均优于现有的最先进技术，并且还在 Apolloscape 和 Mapillary 上展现出了性能的提高。

一种面向域不可知的无监督对抗域自适应规范化层

A Domain Agnostic Normalization Layer for Unsupervised Adversarial  Domain Adaptation

In this work, we address the face parsing task with a Fully-Convolutional
continuous CRF Neural Network (FC-CNN) architecture. In contrast to previous
face parsing methods that apply region-based subnetwork hundreds of times, our
FC-CNN is fully convolutional with high segmentation accuracy. To achieve this
goal, FC-CNN integrates three subnetworks, a unary network, a pairwise network
and a continuous Conditional Random Field (C-CRF) network into a unified
framework. The high-level semantic information and low-level details across
different convolutional layers are captured by the convolutional and
deconvolutional structures in the unary network. The semantic edge context is
learnt by the pairwise network branch to construct pixel-wise affinity. Based
on a differentiable superpixel pooling layer and a differentiable C-CRF layer,
the unary network and pairwise network are combined via a novel continuous CRF
network to achieve spatial consistency in both training and test procedure of a
deep neural network. Comprehensive evaluations on LFW-PL and HELEN datasets
demonstrate that FC-CNN achieves better performance over the other
state-of-arts for accurate face labeling on challenging images.

本文提出了一种基于全卷积网络和连续条件随机场的人脸分割方法，并在多个数据集上进行了全面评估，证明了该方法相对于其他方法具有更好的性能。

基于完全卷积连续 CRF 神经网络的人脸解析

Face Parsing via a Fully-Convolutional Continuous CRF Neural Network

Semantic segmentation is critical to image content understanding and object
localization. Recent development in fully-convolutional neural network (FCN)
has enabled accurate pixel-level labeling. One issue in previous works is that
the FCN based method does not exploit the object boundary information to
delineate segmentation details since the object boundary label is ignored in
the network training. To tackle this problem, we introduce a double branch
fully convolutional neural network, which separates the learning of the
desirable semantic class labeling with mask-level object proposals guided by
relabeled boundaries. This network, called object boundary guided FCN
(OBG-FCN), is able to integrate the distinct properties of object shape and
class features elegantly in a fully convolutional way with a designed masking
architecture. We conduct experiments on the PASCAL VOC segmentation benchmark,
and show that the end-to-end trainable OBG-FCN system offers great improvement
in optimizing the target semantic segmentation quality.

本文介绍了一种名为 OBG-FCN 的全卷积神经网络，它能够利用物体边界信息以完成更为精细的语义分割，其在 PASCAL VOC 分割基准测试上得到了显著的优化。