Edge crossings in drawings of bipartite graphs

Systems engineers have recently shown interest in algorithms for drawing directed graphs so that they are easy to understand and remember. Each of the commonly used methods has a step which aims to adjust the drawing to decrease the number of arc crossings. We show that the most popular strategy involves an NP-complete problem regarding the minimization of the number of arcs in crossings in a bipartite graph. The performance of the commonly employed barycenter heuristic for this problem is analyzed. An alternative method, the median heuristic, is proposed and analyzed. The new method is shown to compare favorably with the old in terms of performance guarantees. As a bonus, we show that the median heuristic performs well with regard to the total length of the arcs in the drawing.     

Feature generation based on relation learning and image partition for occluded person re-identification

In order to solve the challenging tasks of person re-identification(Re-ID) in occluded scenarios, we propose a novel approach which divides local units by forming high-level semantic information of pedestrians and generates features of occluded parts. The approach uses CNN and pose estimation to extract the feature map and key points, and a graph convolutional network to learn the relation of key points. Specifically, we design a Generating Local Part (GLP) module to divide the feature map into different units. Based on different occluded conditions, the partition mode of GLP has high flexibility and variability. The features of the non-occluded parts are clustered into an intermediate node, and then the spatially correlated features of the occluded parts are generated according to the de-clustering operation. We conduct experiments on both the occluded and the holistic datasets to demonstrate its effectiveness.     

基于One-Shot聚合自编码器的图表示学习

自编码器（AE）是一种高效的图数据表示学习模型,但大多数图自编码器（GAE）为浅层模型,其效率会随着隐藏层的增加而降低。针对上述问题,提出基于One-Shot聚合（OSA）和指数线性（ELU）函数的GAE模型OSA-GAE和图变分自编码器模型OSA-VGAE。首先,利用多层图卷积网络（GCN）构建编码器,并引入OSA和ELU函数;然后,在解码阶段使用内积解码器恢复图的拓扑结构;此外,为了防止模型训练过程中的参数过拟合,在损失函数中引入正则化项。实验结果表明,OSA和ELU函数可以有效提高深层GAE的性能,改善模型的梯度信息传递。在使用6层GCN时,基准引文数据集PubMed的链接预测任务中,深层OSA-VGAE相较于原始的VGAE在ROC曲线下的面积（AUC）和平均精度（AP）上分别提升了8.67和6.85个百分点,深层OSA-GAE相较于原始的GAE在AP和AUC上分别提升了6.82和4.39个百分点。     

基于高分辨率网络和图卷积网络的三维人体重建模型

针对单目图像重建人体时出现的头部姿态翻转和图像特征间隐式空间线索缺失的问题,提出了一种基于高分辨率网络（HRNet）和图卷积网络（GCN）的三维人体重建模型。首先利用HRNet和残差块作为主干网络从原始图像中提取丰富的人体特征信息,然后使用GCN来捕获特征之间隐式的空间线索以获得空间精确的特征表示,最后使用此特征来预测多人线性蒙皮模型（SMPL）的参数以得到更加准确的重建结果;同时为了有效解决人体头部姿态翻转的问题,对SMPL的关节点重新进行了定义,在原有关节的基础上增加对头部关节点的定义。实验结果表明,所提模型能够准确地重建出三维人体,在2D数据集LSP上的重建准确率达到了92.41%,在3D数据集MPI-INF-3DHP上的关节误差和重建误差也大幅降低,平均误差仅分别为97.73 mm和64.63 mm,验证了所提模型在人体重建领域的有效性。     

融合用户需求和边界约束的平面图生成算法

平面图设计是房屋设计的重要过程,而现有的自动化平面图设计方法缺乏考虑用户需求和建筑边界的共同约束,存在生成房间形状缺角、房间之间遮挡严重以及房间超越边界的布局不合理问题。针对上述问题,提出一种融合用户需求和边界约束的房屋平面图生成对抗网络（GBC-GAN）,它由约束布局生成器和房间关系鉴别器构成。首先,将用户指定的房屋布局需求（包括房间数量和类型以及房屋之间的邻接方位关系）转化为约束关系图结构,之后对建筑边界和约束关系图分别编码并进行特征融合;然后,在约束布局生成器中引入边界框预测模块以将平面图生成问题转化为各房间对象边界框生成问题,并利用几何边界优化损失来解决房间之间遮挡严重、房间超越边界的问题;最后,将房间边界框布局和约束关系图输入到房间关系鉴别器训练生成符合房间对象及其关系的平面图布局。在大型真实建筑数据集RPLAN上,该方法的弗雷歇距离（FID）和结构相似性指数（SSIM）比House-GAN方法分别提升了4.39%和2.3%。实验结果表明,在不同用户需求和边界限制条件下,所提方法提高了房屋平面图的合理性和真实性。     

一种基于图注意力网络的异质信息网络表示学习框架

常用的异质信息网络有知识图谱和具有简单模式层的异质信息网络,它们的表示学习通常遵循不同的方法。该文总结了知识图谱和具有简单模式层的异质信息网络之间的异同,提出了一个通用的异质信息网络表示学习框架。该文提出的框架可以分为3个部分：基础向量模型,基于图注意力网络的传播模型以及任务模型。基础向量模型用于学习基础的网络向量;传播模型通过堆叠注意力层学习网络的高阶邻居特征;可更换的任务模型适用于不同的应用场景。与基准模型相比,该文所提框架在知识图谱的链接预测任务和异质信息网络的节点分类任务中都取得了相对不错的效果。     

Single image shadow detection via uncertainty analysis and GCN-based refinement strategy

Learning-based shadow detection methods have achieved an impressive performance, while these works still struggle on complex scenes, especially ambiguous soft shadows. To tackle this issue, this work proposes an efficient shadow detection network (ESDNet) and then applies uncertainty analysis and graph convolutional networks for detection refinement. Specifically, we first aggregate global information from high-level features and harvest shadow details in low-level features for obtaining an initial prediction. Secondly, we analyze the uncertainty of our ESDNet for an input shadow image and then take its intensity, expectation, and entropy into account to formulate a semi-supervised graph learning problem. Finally, we solve this problem by training a graph convolution network to obtain the refined detection result for every training image. To evaluate our method, we conduct extensive experiments on several benchmark datasets, i.e., SBU, UCF, ISTD, and even on soft shadow scenes. Experimental results demonstrate that our strategy can improve shadow detection performance by suppressing the uncertainties of false positive and false negative regions, achieving state-of-the-art results.     

Efficient graph attentional network for 3D object detection from Frustum-based LiDAR point clouds

LiDAR-based 3D object detection is important for autonomous driving scene perception, but point clouds produced by LiDAR are irregular and unstructured in nature, and cannot be adopted by the conventional Convolutional Neural Networks (CNN). Recently, Graph Convolutional Networks (GCN) has been proved as an ideal way to handle non-Euclidean structure data, as well as for point cloud processing. However, GCN involves massive computation for searching adjacent nodes, and the heavy computational cost limits its applications in processing large-scale LiDAR point cloud in autonomous driving. In this work, we adopt a frustum-based point cloud-image fusion scheme to reduce the amount of LiDAR point clouds, thus making the GCN-based large-scale LiDAR point clouds feature learning feasible. On this basis, we propose an efficient graph attentional network to accomplish the goal of 3D object detection in autonomous driving, which can learn features from raw LiDAR point cloud directly without any conversions. We evaluate the model on the public KITTI benchmark dataset, the 3D detection mAP is 63.72% on KITTI Cars, Pedestrian and Cyclists, and the inference speed achieves 7.9 fps on a single GPU, which is faster than other methods of the same type.