Deep convolutional neural networks (CNNs) have been immensely successful in
many high-level computer vision tasks given large labeled datasets. However,
for video semantic object segmentation, a domain where labels are scarce,
effectively exploiting the representation power of CNN with limited training
data remains a challenge. Simply borrowing the existing pretrained CNN image
recognition model for video segmentation task can severely hurt performance. We
propose a semi-supervised approach to adapting CNN image recognition model
trained from labeled image data to the target domain exploiting both semantic
evidence learned from CNN, and the intrinsic structures of video data. By
explicitly modeling and compensating for the domain shift from the source
domain to the target domain, this proposed approach underpins a robust semantic
object segmentation method against the changes in appearance, shape and
occlusion in natural videos. We present extensive experiments on challenging
datasets that demonstrate the superior performance of our approach compared
with the state-of-the-art methods.

该论文提出了一种半监督方法来适应经过标记的图像数据训练的 CNN 图像识别模型到目标域，利用 CNN 学到的语义证据和视频数据的内在结构进行视频语义对象分割，显式地建模并补偿从源域到目标域的领域转移，并展示了我们的方法在具有挑战性的数据集上相对于现有方法的卓越性能。

弱标注语义视频物体分割的半监督域适应

Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video  Object Segmentation

Recently, significant improvement has been made on semantic object
segmentation due to the development of deep convolutional neural networks
(DCNNs). Training such a DCNN usually relies on a large number of images with
pixel-level segmentation masks, and annotating these images is very costly in
terms of both finance and human effort. In this paper, we propose a simple to
complex (STC) framework in which only image-level annotations are utilized to
learn DCNNs for semantic segmentation. Specifically, we first train an initial
segmentation network called Initial-DCNN with the saliency maps of simple
images (i.e., those with a single category of major object(s) and clean
background). These saliency maps can be automatically obtained by existing
bottom-up salient object detection techniques, where no supervision information
is needed. Then, a better network called Enhanced-DCNN is learned with
supervision from the predicted segmentation masks of simple images based on the
Initial-DCNN as well as the image-level annotations. Finally, more pixel-level
segmentation masks of complex images (two or more categories of objects with
cluttered background), which are inferred by using Enhanced-DCNN and
image-level annotations, are utilized as the supervision information to learn
the Powerful-DCNN for semantic segmentation. Our method utilizes $40$K simple
images from Flickr.com and 10K complex images from PASCAL VOC for step-wisely
boosting the segmentation network. Extensive experimental results on PASCAL VOC
2012 segmentation benchmark well demonstrate the superiority of the proposed
STC framework compared with other state-of-the-arts.

本文提出了一种简单有效的框架（STC）方法，在此方法中，只利用基于图像级别注释的简单图像对已训练的深度卷积神经网络（DCNN）进行语义分割。通过本文的方法，可以有效减少由于人工标记带来的显著算力和人力成本，使语义对象分割的效果得到显著提升。

STC: 一种简单到复杂的弱监督语义分割框架

STC: A Simple to Complex Framework for Weakly-supervised Semantic  Segmentation

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。