In this paper we propose a CNN architecture for semantic image segmentation.
We introduce a new 'bilateral inception' module that can be inserted in
existing CNN architectures and performs bilateral filtering, at multiple
feature-scales, between superpixels in an image. The feature spaces for
bilateral filtering and other parameters of the module are learned end-to-end
using standard backpropagation techniques. The bilateral inception module
addresses two issues that arise with general CNN segmentation architectures.
First, this module propagates information between (super) pixels while
respecting image edges, thus using the structured information of the problem
for improved results. Second, the layer recovers a full resolution segmentation
result from the lower resolution solution of a CNN. In the experiments, we
modify several existing CNN architectures by inserting our inception module
between the last CNN (1x1 convolution) layers. Empirical results on three
different datasets show reliable improvements not only in comparison to the
baseline networks, but also in comparison to several dense-pixel prediction
techniques such as CRFs, while being competitive in time.

本文提出了使用 CNN 进行语义图像分割的体系结构，使用新的 “双边启发式” 模块解决了通用 CNN 分割结构中出现的两个问题，并在三个不同数据集上得到了可靠的改进。

使用双边 Inception 的超像素卷积网络

Superpixel Convolutional Networks using Bilateral Inceptions

Deep learning, in particular Convolutional Neural Network (CNN), has achieved
promising results in face recognition recently. However, it remains an open
question: why CNNs work well and how to design a 'good' architecture. The
existing works tend to focus on reporting CNN architectures that work well for
face recognition rather than investigate the reason. In this work, we conduct
an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a
common ground to make our work easily reproducible. Specifically, we use public
database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing
CNNs trained on private databases. We propose three CNN architectures which are
the first reported architectures trained using LFW data. This paper
quantitatively compares the architectures of CNNs and evaluate the effect of
different implementation choices. We identify several useful properties of
CNN-FRS. For instance, the dimensionality of the learned features can be
significantly reduced without adverse effect on face recognition accuracy. In
addition, traditional metric learning method exploiting CNN-learned features is
evaluated. Experiments show two crucial factors to good CNN-FRS performance are
the fusion of multiple CNNs and metric learning. To make our work reproducible,
source code and models will be made publicly available.

本文通过使用公共的数据集 LFW，提出了三种最近的第一代 CNN 结构，研究了卷积神经网络应用于人脸识别的原因和设计，发现将多个 CNN 结构及度量学习的融合，可以提高 CNN-FRS 的性能并使得学习特征的维度可显著减小。