Deep Convolutional Neural Networks (DCNNs) have recently shown state of the
art performance in high level vision tasks, such as image classification and
object detection. This work brings together methods from DCNNs and
probabilistic graphical models for addressing the task of pixel-level
classification (also called "semantic image segmentation"). We show that
responses at the final layer of DCNNs are not sufficiently localized for
accurate object segmentation. This is due to the very invariance properties
that make DCNNs good for high level tasks. We overcome this poor localization
property of deep networks by combining the responses at the final DCNN layer
with a fully connected Conditional Random Field (CRF). Qualitatively, our
"DeepLab" system is able to localize segment boundaries at a level of accuracy
which is beyond previous methods. Quantitatively, our method sets the new
state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching
71.6% IOU accuracy in the test set. We show how these results can be obtained
efficiently: Careful network re-purposing and a novel application of the 'hole'
algorithm from the wavelet community allow dense computation of neural net
responses at 8 frames per second on a modern GPU.

该研究论文结合深度卷积神经网络和概率图模型的方法来解决像素级分类（语义图像分割）任务中深层网络往往无法精确定位目标分割的问题，并通过将深层网络的响应与全连接条件随机场相结合，提出了一种 DeepLab 系统。定量实验表明，该方法在 PASCAL VOC-2012 语义图像分割任务中，IOU 精度可达 71.6％。