This paper proposes a novel self-supervised based Cut-and-Paste GAN to
perform foreground object segmentation and generate realistic composite images
without manual annotations. We accomplish this goal by a simple yet effective
self-supervised approach coupled with the U-Net based discriminator. The
proposed method extends the ability of the standard discriminators to learn not
only the global data representations via classification (real/fake) but also
learn semantic and structural information through pseudo labels created using
the self-supervised task. The proposed method empowers the generator to create
meaningful masks by forcing it to learn informative per-pixel as well as global
image feedback from the discriminator. Our experiments demonstrate that our
proposed method significantly outperforms the state-of-the-art methods on the
standard benchmark datasets.

本文提出了一种基于自监督的 Cut-and-Paste GAN 方法，可以在不需要手动注释标记的情况下进行前景对象分割和生成逼真的合成图像，并且采用了 U-Net 判别器，通过伪标签学习语义和结构信息来扩展标准判别器的能力。实验结果表明，该方法显著优于标准基准数据集上的现有方法。

自监督剪切与粘贴生成对抗网络用于物体分割

SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network  for Object Segmentation

Even after decades of research, dynamic scene background reconstruction and
foreground object segmentation are still considered as open problems due
various challenges such as illumination changes, camera movements, or
background noise caused by air turbulence or moving trees. We propose in this
paper to model the background of a frame sequence as a low dimensional manifold
using an autoencoder and compare the reconstructed background provided by this
autoencoder with the original image to compute the foreground/background
segmentation masks. The main novelty of the proposed model is that the
autoencoder is also trained to predict the background noise, which allows to
compute for each frame a pixel-dependent threshold to perform the foreground
segmentation. Although the proposed model does not use any temporal or motion
information, it exceeds the state of the art for unsupervised background
subtraction on the CDnet 2014 and LASIESTA datasets, with a significant
improvement on videos where the camera is moving. It is also able to perform
background reconstruction on some non-video image datasets.

本文提出了一种使用自编码器模拟场景背景的低维流形，通过与原始图像进行对比实现前景 / 背景分割，自编码器同时用于预测背景噪音，从而计算每一帧的像素相关阈值进行前景分割。该模型在 CDNet 2014 和 LASIESTA 数据集上超越了现有技术，且能够在某些非视频图像数据集上实现背景重构。

基于自编码器的背景重建和前景分割及背景噪声估计

Autoencoder-based background reconstruction and foreground segmentation  with background noise estimation

We propose an end-to-end learning framework for generating foreground object
segmentations. Given a single novel image, our approach produces pixel-level
masks for all "object-like" regions---even for object categories never seen
during training. We formulate the task as a structured prediction problem of
assigning foreground/background labels to all pixels, implemented using a deep
fully convolutional network. Key to our idea is training with a mix of
image-level object category examples together with relatively few images with
boundary-level annotations. Our method substantially improves the
state-of-the-art on foreground segmentation for ImageNet and MIT Object
Discovery datasets. Furthermore, on over 1 million images, we show that it
generalizes well to segment object categories unseen in the foreground maps
used for training. Finally, we demonstrate how our approach benefits image
retrieval and image retargeting, both of which flourish when given our
high-quality foreground maps.

本文提出了一种端到端的学习框架，其中使用深度卷积网络实现了将前景 / 背景标签分配给所有像素的结构化预测，在无需看到训练期间未曾见过的物体类别的情况下生成了像素级的前景对象分割，并在 ImageNet 和 MIT ObjectDiscovery 数据集上显著改善了前景分割的最新技术成果。我们最后展示了该方法如何使图像检索和图像重排具备更好的应用前景图。