Semantic instance segmentation remains a challenging task. In this work we
propose to tackle the problem with a discriminative loss function, operating at
the pixel level, that encourages a convolutional network to produce a
representation of the image that can easily be clustered into instances with a
simple post-processing step. The loss function encourages the network to map
each pixel to a point in feature space so that pixels belonging to the same
instance lie close together while different instances are separated by a wide
margin. Our approach of combining an off-the-shelf network with a principled
loss function inspired by a metric learning objective is conceptually simple
and distinct from recent efforts in instance segmentation. In contrast to
previous works, our method does not rely on object proposals or recurrent
mechanisms. A key contribution of our work is to demonstrate that such a simple
setup without bells and whistles is effective and can perform on par with more
complex methods. Moreover, we show that it does not suffer from some of the
limitations of the popular detect-and-segment approaches. We achieve
competitive performance on the Cityscapes and CVPPP leaf segmentation
benchmarks.

本文提出利用判别式损失函数的像素级卷积神经网络来实现简单的图像实例分割，不需要借助物体提议或循环机制，仅仅通过一个后处理步骤即可完成聚类，简单有效且具有竞争性。

具有判别性损失函数的语义实例分割

Semantic Instance Segmentation with a Discriminative Loss Function

Traditional Scene Understanding problems such as Object Detection and
Semantic Segmentation have made breakthroughs in recent years due to the
adoption of deep learning. However, the former task is not able to localise
objects at a pixel level, and the latter task has no notion of different
instances of objects of the same class. We focus on the task of Instance
Segmentation which recognises and localises objects down to a pixel level. Our
model is based on a deep neural network trained for semantic segmentation. This
network incorporates a Conditional Random Field with end-to-end trainable
higher order potentials based on object detector outputs. This allows us to
reason about instances from an initial, category-level semantic segmentation.
Our simple method effectively leverages the great progress recently made in
semantic segmentation and object detection. The accurate instance-level
segmentations that our network produces is reflected by the considerable
improvements obtained over previous work.

该研究采用基于深度神经网络的条件随机场方法，实现了物体的像素级别识别和定位。通过结合语义分割和目标检测技术，有效提高了实例级分割的准确性。

基于深度高阶 CRF 的自下而上的实例分割

Bottom-up Instance Segmentation using Deep Higher-Order CRFs

We present an image-conditional image generation model. The model transfers
an input domain to a target domain in semantic level, and generates the target
image in pixel level. To generate realistic target images, we employ the
real/fake-discriminator as in Generative Adversarial Nets, but also introduce a
novel domain-discriminator to make the generated image relevant to the input
image. We verify our model through a challenging task of generating a piece of
clothing from an input image of a dressed person. We present a high quality
clothing dataset containing the two domains, and succeed in demonstrating
decent results.

本文提出了一种基于图像条件生成的模型，该模型在语义级别上将输入域转换为目标域，并在像素级别上生成目标图像。为了生成逼真的目标图像，我们采用了生成对抗网络中的真实 / 假的鉴别器，并引入了新颖的域鉴别器，使生成的图像与输入图像相关。我们通过生成一个穿着人物的输入图像来验证我们的模型，同时提出了一个高质量的服装数据集，成功地展示了不错的结果。