Segmenting semantic objects from images and parsing them into their
respective semantic parts are fundamental steps towards detailed object
understanding in computer vision. In this paper, we propose a joint solution
that tackles semantic object and part segmentation simultaneously, in which
higher object-level context is provided to guide part segmentation, and more
detailed part-level localization is utilized to refine object segmentation.
Specifically, we first introduce the concept of semantic compositional parts
(SCP) in which similar semantic parts are grouped and shared among different
objects. A two-channel fully convolutional network (FCN) is then trained to
provide the SCP and object potentials at each pixel. At the same time, a
compact set of segments can also be obtained from the SCP predictions of the
network. Given the potentials and the generated segments, in order to explore
long-range context, we finally construct an efficient fully connected
conditional random field (FCRF) to jointly predict the final object and part
labels. Extensive evaluation on three different datasets shows that our
approach can mutually enhance the performance of object and part segmentation,
and outperforms the current state-of-the-art on both tasks.

本文提出了一种同时处理语义对象和部件分割的联合解决方案，其中提供了更高级的对象级上下文来引导部件分割，并利用更详细的部件级定位来改进对象分割。所提出的方法在三个不同的数据集上进行了广泛的评估，证明它可以相互增强对象和部件分割的性能，并在两个任务上都优于现有技术水平。