Graph Signal Processing for Geometric Data and Beyond: Theory and Applications
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；InterDigital（InterDigital公司）；Harbin Institute of Technology（哈尔滨工业大学）；National Tsing Hua University（国立清华大学）；Industrial Technology Research Institute（工业技术研究院）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
Geometric data acquired from real-world scenes, e.g., 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc. Due to irregular sampling patterns of most geometric data, traditional image/video processing methodologies are limited, while Graph Signal Processing (GSP) -- a fast-developing field in the signal processing community -- enables processing signals that reside on irregular domains and plays a critical role in numerous applications of geometric data from low-level processing to high-level analysis. To further advance the research in this field, we provide the first timely and comprehensive overview of GSP methodologies for geometric data in a unified manner by bridging the connections between geometric data and graphs, among the various geometric data modalities, and with spectral/nodal graph filtering techniques. We also discuss the recently developed Graph Neural Networks (GNNs) and interpret the operation of these networks from the perspective of GSP. We conclude with a brief discussion of open problems and challenges.
Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)
Worcester Polytechnic Institute（伍斯特理工学院）
为了学习可区分的模式，最近的大多数车辆重新识别（ReID）工作都在努力重新开发官方基准以提供各种监督，这需要大量的人力。在本文中，我们寻求实现类似的目标，但不涉及更多的人力。为此，我们引入了一个新的框架，它成功地编码几何局部特征和全局表示来区分车辆实例，仅通过官方ID标签的监督进行优化。具体来说，鉴于我们的洞察力，ReID中的对象共享相似的几何特征，我们建议借用自监督表示学习，以促进几何特征发现。为了浓缩这些特征，我们引入了一个可解释的注意力模块，其核心是局部最大值聚合而不是全自动学习，其机制是完全可以理解的，其响应图是物理上合理的。据我们所知，我们是第一个执行自监督学习来发现几何特征的人。我们对三个最流行的车辆ReID数据集进行了全面的实验，即，VeRi-776、CityFlow-ReID和VehicleID。我们报告我们的国家的最先进的（SOTA）的性能和有前途的可视化结果。我们还展示了我们的方法在其他ReID相关任务上的出色可扩展性，即，个人ReID和多目标多摄像头（MTMC）车辆跟踪。该代码可在https://github.com/ ming 1993 li/Self-supervised-Geometric上获得。
Department of Computer and Information Sciences, Niagara University（尼加拉瓜大学计算机与信息科学系）；Department of Computer Science and Engineering, State University of New York at Buffalo（纽约州立大学布法罗分校计算机科学与工程系）
UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement
Shenzhen Graduate School, Peking University（北京大学深圳研究生院）；The Hong Kong University of Science（香港科学大学）；National University of Singapore（新加坡国立大学）；S-Lab, Nanyang Technological University（南洋理工大学S实验室）
