Pixel-level labelling tasks, such as semantic segmentation and depth estimation from single RGB image, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks. To solve this problem, we introduce a new form of convolutional neural network, called CRF-RNN, which expresses a Conditional Random Field (CRF) as a Recurrent Neural Network (RNN). Our short network can be plugged in as a part of a deep Convolutional Neural Network (CNN) to obtain an end-to-end system that has desirable properties of both CNNs and CRFs. Importantly, our system fully integrates CRF modelling with CNNs, making it possible to train the whole system end-to-end with the usual back-propagation algorithm. We apply this framework to the problem of semantic image segmentation, obtaining competitive results with the state-of-the-art without the need of introducing any post-processing method for object delineation.

介绍了一种名为CRF-RNN的新型卷积神经网络，该网络将卷积神经网络（CNN）和基于CRFs的概率图建模相结合，用于语义图像分割任务，结果在Pascal VOC 2012分割基准测试上获得了最好的结果。

条件随机场作为循环神经网络