Training a deep network to perform semantic segmentation requires large
amounts of labeled data. To alleviate the manual effort of annotating real
images, researchers have investigated the use of synthetic data, which can be
labeled automatically. Unfortunately, a network trained on synthetic data
performs relatively poorly on real images. While this can be addressed by
domain adaptation, existing methods all require having access to real images
during training. In this paper, we introduce a drastically different way to
handle synthetic images that does not require seeing any real images at
training time. Our approach builds on the observation that foreground and
background classes are not affected in the same manner by the domain shift, and
thus should be treated differently. In particular, the former should be handled
in a detection-based manner to better account for the fact that, while their
texture in synthetic images is not photo-realistic, their shape looks natural.
Our experiments evidence the effectiveness of our approach on Cityscapes and
CamVid with models trained on synthetic data only.

本文提出了一种无需真实图像数据的方法，通过修改前景和背景类的训练方法来适应合成图像，有效地训练出了适用于城市街景和驾驶场景的图像语义分割模型。

城市场景语义分割中合成数据的有效使用

Effective Use of Synthetic Data for Urban Scene Semantic Segmentation

Foreground detection has been widely studied for decades due to its
importance in many practical applications. Most of the existing methods assume
foreground and background show visually distinct characteristics and thus the
foreground can be detected once a good background model is obtained. However,
there are many situations where this is not the case. Of particular interest in
video surveillance is the camouflage case. For example, an active attacker
camouflages by intentionally wearing clothes that are visually similar to the
background. In such cases, even given a decent background model, it is not
trivial to detect foreground objects. This paper proposes a texture guided
weighted voting (TGWV) method which can efficiently detect foreground objects
in camouflaged scenes. The proposed method employs the stationary wavelet
transform to decompose the image into frequency bands. We show that the small
and hardly noticeable differences between foreground and background in the
image domain can be effectively captured in certain wavelet frequency bands. To
make the final foreground decision, a weighted voting scheme is developed based
on intensity and texture of all the wavelet bands with weights carefully
designed. Experimental results demonstrate that the proposed method achieves
superior performance compared to the current state-of-the-art results.

该论文提出了一种纹理引导加权投票（TGWV）方法，可以有效地检测伪装场景中的前景对象。该方法利用静态小波变换将图像分解成频带，并根据所有小波频带的强度和纹理设计了加权投票方案，结果表明该方法的性能优于当前的最新结果。