We propose a novel solution for unpaired image-to-image (I2I) translation. To
translate complex images with a wide range of objects to a different domain,
recent approaches often use the object annotations to perform per-class
source-to-target style mapping. However, there remains a point for us to
exploit in the I2I. An object in each class consists of multiple components,
and all the sub-object components have different characteristics. For example,
a car in CAR class consists of a car body, tires, windows and head and tail
lamps, etc., and they should be handled separately for realistic I2I
translation. The simplest solution to the problem will be to use more detailed
annotations with sub-object component annotations than the simple object
annotations, but it is not possible. The key idea of this paper is to bypass
the sub-object component annotations by leveraging the original style of the
input image because the original style will include the information about the
characteristics of the sub-object components. Specifically, for each pixel, we
use not only the per-class style gap between the source and target domains but
also the pixel's original style to determine the target style of a pixel. To
this end, we present Style Harmonization for unpaired I2I translation (SHUNIT).
Our SHUNIT generates a new style by harmonizing the target domain style
retrieved from a class memory and an original source image style. Instead of
direct source-to-target style mapping, we aim for source and target styles
harmonization. We validate our method with extensive experiments and achieve
state-of-the-art performance on the latest benchmark sets. The source code is
available online: this https URL

本文提出了一种新颖的解决方案来实现非配对图像到图像的翻译，可以通过风格协调的方式处理不同类别中物体的多个组件，以获得最先进的性能。

SHUNIT：无配对图像转换的风格协调

SHUNIT: Style Harmonization for Unpaired Image-to-Image Translation

We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM is
the largest dataset of 3D real-world spaces with densely annotated semantics
that is currently available to the academic community. It consists of 142,646
object instance annotations across 216 3D spaces and 3,100 rooms within those
spaces. The scale, quality, and diversity of object annotations far exceed
those of prior datasets. A key difference setting apart HM3DSEM from other
datasets is the use of texture information to annotate pixel-accurate object
boundaries. We demonstrate the effectiveness of HM3DSEM dataset for the Object
Goal Navigation task using different methods. Policies trained using HM3DSEM
perform outperform those trained on prior datasets. Introduction of HM3DSEM in
the Habitat ObjectNav Challenge lead to an increase in participation from 400
submissions in 2021 to 1022 submissions in 2022.

介绍了规模、质量和多样性都高于以往数据集的 HM3DSEM 数据集，该数据集使用纹理信息进行像素精度的对象边界注释，使用该数据集训练的策略在 Object Goal Navigation 任务上表现优异，引入该数据集还带来了 Habitat ObjectNav Challenge 参与者数量的增加。

Habitat-Matterport 3D 语义数据集

Habitat-Matterport 3D Semantics Dataset

State-of-the-art learning based boundary detection methods require extensive
training data. Since labelling object boundaries is one of the most expensive
types of annotations, there is a need to relax the requirement to carefully
annotate images to make both the training more affordable and to extend the
amount of training data. In this paper we propose a technique to generate
weakly supervised annotations and show that bounding box annotations alone
suffice to reach high-quality object boundaries without using any
object-specific boundary annotations. With the proposed weak supervision
techniques we achieve the top performance on the object boundary detection
task, outperforming by a large margin the current fully supervised
state-of-the-art methods.

本文提出了一项弱监督技术用于生成边界检测的标注，仅使用边界框的标注数据即可在目标边界检测任务中达到高性能。该方法在边界检测任务上表现优于当前最先进的完全监督方法。