Semantic segmentation tasks can be well modeled by Markov Random Field (MRF).
This paper addresses semantic segmentation by incorporating high-order
relations and mixture of label contexts into MRF. Unlike previous works that
optimized MRFs using iterative algorithm, we solve MRF by proposing a
Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which
enables deterministic end-to-end computation in a single forward pass.
Specifically, DPN extends a contemporary CNN to model unary terms and
additional layers are devised to approximate the mean field (MF) algorithm for
pairwise terms. It has several appealing properties. First, different from the
recent works that required many iterations of MF during back-propagation, DPN
is able to achieve high performance by approximating one iteration of MF.
Second, DPN represents various types of pairwise terms, making many existing
models as its special cases. Furthermore, pairwise terms in DPN provide a
unified framework to encode rich contextual information in high-dimensional
data, such as images and videos. Third, DPN makes MF easier to be parallelized
and speeded up, thus enabling efficient inference. DPN is thoroughly evaluated
on standard semantic image/video segmentation benchmarks, where a single DPN
model yields state-of-the-art segmentation accuracies on PASCAL VOC 2012,
Cityscapes dataset and CamVid dataset.

本论文提出了一种名为 Deep Parsing Network (DPN) 的卷积神经网络，通过将高阶关系和标签上下文的混合引入到 MRF 模型中，该网络能够在一次前向传递中实现确定性的端到端计算。与先前使用迭代算法优化 MRF 不同的是，该论文通过 DPN 精确求解 MRF，并能够在近似一次 MF 迭代的情况下达到较高的分割精度。此外，DPN 使得 MF 更容易被并行化和加速，从而可以实现高效的推理。

深度学习马尔可夫随机场用于语义分割

Deep Learning Markov Random Field for Semantic Segmentation

This paper addresses semantic image segmentation by incorporating rich
information into Markov Random Field (MRF), including high-order relations and
mixture of label contexts. Unlike previous works that optimized MRFs using
iterative algorithm, we solve MRF by proposing a Convolutional Neural Network
(CNN), namely Deep Parsing Network (DPN), which enables deterministic
end-to-end computation in a single forward pass. Specifically, DPN extends a
contemporary CNN architecture to model unary terms and additional layers are
carefully devised to approximate the mean field algorithm (MF) for pairwise
terms. It has several appealing properties. First, different from the recent
works that combined CNN and MRF, where many iterations of MF were required for
each training image during back-propagation, DPN is able to achieve high
performance by approximating one iteration of MF. Second, DPN represents
various types of pairwise terms, making many existing works as its special
cases. Third, DPN makes MF easier to be parallelized and speeded up in
Graphical Processing Unit (GPU). DPN is thoroughly evaluated on the PASCAL VOC
2012 dataset, where a single DPN model yields a new state-of-the-art
segmentation accuracy.

该研究论文通过将高阶关系和标签上下文混合等丰富信息融入到马尔可夫随机场中，提出了一种名为深度解析网络的卷积神经网络，通过一次前向运算实现确定性的端到端计算。在 PASCAL VOC 2012 数据集上经过全面评估，表现出卓越的分割准确性。

深层解析网络语义图像分割

Semantic Image Segmentation via Deep Parsing Network

The mean field algorithm is a widely used approximate inference algorithm for
graphical models whose exact inference is intractable. In each iteration of
mean field, the approximate marginals for each variable are updated by getting
information from the neighbors. This process can be equivalently converted into
a feedforward network, with each layer representing one iteration of mean field
and with tied weights on all layers. This conversion enables a few natural
extensions, e.g. untying the weights in the network. In this paper, we study
these mean field networks (MFNs), and use them as inference tools as well as
discriminative models. Preliminary experiment results show that MFNs can learn
to do inference very efficiently and perform significantly better than mean
field as discriminative models.

本文针对平均场算法在应对不可精确推断的图模型中的广泛使用进行研究，并将该算法转化为具有多层结构和绑定权重的前向网络以进行一些自然的扩展，包括权重不绑定和 MFNs 作为推理工具和判别模型，最终的实验结果表明，在作为判别模型时，MFNs 可以比平均场算法更有效地进行推理并具有更好的性能。