State-of-the-art results of semantic segmentation are established by Fully
Convolutional neural Networks (FCNs). FCNs rely on cascaded convolutional and
pooling layers to gradually enlarge the receptive fields of neurons, resulting
in an indirect way of modeling the distant contextual dependence. In this work,
we advocate the use of spatially recurrent layers (i.e. ReNet layers) which
directly capture global contexts and lead to improved feature representations.
We demonstrate the effectiveness of ReNet layers by building a Naive deep ReNet
(N-ReNet), which achieves competitive performance on Stanford Background
dataset. Furthermore, we integrate ReNet layers with FCNs, and develop a novel
Hybrid deep ReNet (H-ReNet). It enjoys a few remarkable properties, including
full-image receptive fields, end-to-end training, and efficient network
execution. On the PASCAL VOC 2012 benchmark, the H-ReNet improves the results
of state-of-the-art approaches Piecewise, CRFasRNN and DeepParsing by 3.6%,
2.3% and 0.2%, respectively, and achieves the highest IoUs for 13 out of the 20
object classes.

探讨了如何在图像识别的语义分割领域中，采用 ReNet 层代替全卷积神经网络中的级联卷积和池化层来使局部感受野具有全局信息，并开发了一种新的混合深层 ReNet（H-ReNet）模型，经实验证明其在 PASCAL VOC 2012 基准测试中的识别准确度比 Piecewise、CRFasRNN 和 DeepParsing 高 3.6%、2.3% 和 0.2%。