Deep convolutional neural networks (CNN) have achieved great success. On the
other hand, modeling structural information has been proved critical in many
vision problems. It is of great interest to integrate them effectively. In a
classical neural network, there is no message passing between neurons in the
same layer. In this paper, we propose a CRF-CNN framework which can
simultaneously model structural information in both output and hidden feature
layers in a probabilistic way, and it is applied to human pose estimation. A
message passing scheme is proposed, so that in various layers each body joint
receives messages from all the others in an efficient way. Such message passing
can be implemented with convolution between features maps in the same layer,
and it is also integrated with feedforward propagation in neural networks.
Finally, a neural network implementation of end-to-end learning CRF-CNN is
provided. Its effectiveness is demonstrated through experiments on two
benchmark datasets.

本文提出了 CRF-CNN 框架，用于在深度卷积神经网络中同时模拟输出层和隐藏特征层的结构信息，并应用于人体姿势估计，通过在各层之间的信息传递，采用了卷积和前向传播相结合的方式进行模型的训练，最终在两个基准数据集上验证该模型的有效性。