We propose a novel semantic segmentation algorithm by learning a
deconvolution network. We learn the network on top of the convolutional layers
adopted from VGG 16-layer net. The deconvolution network is composed of
deconvolution and unpooling layers, which identify pixel-wise class labels and
predict segmentation masks. We apply the trained network to each proposal in an
input image, and construct the final semantic segmentation map by combining the
results from all proposals in a simple manner. The proposed algorithm mitigates
the limitations of the existing methods based on fully convolutional networks
by integrating deep deconvolution network and proposal-wise prediction; our
segmentation method typically identifies detailed structures and handles
objects in multiple scales naturally. Our network demonstrates outstanding
performance in PASCAL VOC 2012 dataset, and we achieve the best accuracy
(72.5%) among the methods trained with no external data through ensemble with
the fully convolutional network.

本文提出一种基于学习反卷积网络的新颖语义分割算法，其中反卷积网络由反卷积层和上采样层组成，能够在像素级别上识别图像中的类别标签和预测分割掩模，通过将结果合并得到最终的分割图像，并且可以自然地处理多尺度下的物体和识别细节结构，在 PASCAL VOC 2012 数据集上进行实验，通过与完全卷积网络的集合学习，取得了最好的 72.5% 精度。