Object location is fundamental to panoptic segmentation as it is related to
all things and stuff in the image scene. Knowing the locations of objects in
the image provides clues for segmenting and helps the network better understand
the scene. How to integrate object location in both thing and stuff
segmentation is a crucial problem. In this paper, we propose spatial
information flows to achieve this objective. The flows can bridge all sub-tasks
in panoptic segmentation by delivering the object's spatial context from the
box regression task to others. More importantly, we design four parallel
sub-networks to get a preferable adaptation of object spatial information in
sub-tasks. Upon the sub-networks and the flows, we present a location-aware and
unified framework for panoptic segmentation, denoted as SpatialFlow. We perform
a detailed ablation study on each component and conduct extensive experiments
to prove the effectiveness of SpatialFlow. Furthermore, we achieve
state-of-the-art results, which are $47.9$ PQ and $62.5$ PQ respectively on
MS-COCO and Cityscapes panoptic benchmarks. Code will be available at
this https URL

该研究论文提出了一种称为 SpatialFlow 的基于空间信息流的新型定位感知与统一框架，利用子网络和信息流来解决全景分割中物体位置与场景理解问题，其实现在 MS-COCO 和 Cityscapes 全景基准测试上获得了最先进的成果。

SpatialFlow: 联接全景分割的所有任务

SpatialFlow: Bridging All Tasks for Panoptic Segmentation

Recent advances in convolutional neural networks (CNN) have achieved
remarkable results in locating objects in images. In these networks, the
training procedure usually requires providing bounding boxes or the maximum
number of expected objects. In this paper, we address the task of estimating
object locations without annotated bounding boxes which are typically
hand-drawn and time consuming to label. We propose a loss function that can be
used in any fully convolutional network (FCN) to estimate object locations.
This loss function is a modification of the average Hausdorff distance between
two unordered sets of points. The proposed method has no notion of bounding
boxes, region proposals, or sliding windows. We evaluate our method with three
datasets designed to locate people's heads, pupil centers and plant centers. We
outperform state-of-the-art generic object detectors and methods fine-tuned for
pupil tracking.

本文提出了一种可用于无未注释的边界框的 object location 评估的 Loss function，该方法不需要边界框、区域提议或滑动窗口，并使用 3 个数据集对该方法进行了评估，分别为人头位置、瞳孔中心和植物中心。我们的方法优于最先进的通用对象检测器和针对瞳孔跟踪微调的方法。

无需边界框的物体定位

Locating Objects Without Bounding Boxes

Generative Adversarial Networks (GANs) have recently demonstrated the
capability to synthesize compelling real-world images, such as room interiors,
album covers, manga, faces, birds, and flowers. While existing models can
synthesize images based on global constraints such as a class label or caption,
they do not provide control over pose or object location. We propose a new
model, the Generative Adversarial What-Where Network (GAWWN), that synthesizes
images given instructions describing what content to draw in which location. We
show high-quality 128 x 128 image synthesis on the Caltech-UCSD Birds dataset,
conditioned on both informal text descriptions and also object location. Our
system exposes control over both the bounding box around the bird and its
constituent parts. By modeling the conditional distributions over part
locations, our system also enables conditioning on arbitrary subsets of parts
(e.g. only the beak and tail), yielding an efficient interface for picking part
locations. We also show preliminary results on the more challenging domain of
text- and location-controllable synthesis of images of human actions on the
MPII Human Pose dataset.

Generative Adversarial Networks 已经在绘制逼真的世界图片方面展现了相当可观的能力，现有的模型可以根据类别标签或标题等全局限制生成图像，但不能控制姿势或对象位置，本文提出了一种新的模型 ——Generative Adversarial What-Where Network，它能根据所描述的内容和位置进行图像综合，并在 Caltech-UCSD Birds 数据集上展示了高质量的 128 x 128 图片综合，还可以控制鸟周围的边界框及其组成部分，通过模拟条件分布，我们的系统还能够使各个部分进行控制，同时展示了在 MPII Human Pose 数据集的关于人体动作图像的文本与位置可控合成的初步结果。