SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation

We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal, from which we can directly extract instance segmentation results. Important to the effectiveness of SGPN is its novel representation of 3D instance segmentation results in the form of a similarity matrix that indicates the similarity between each pair of points in embedded feature space, thus producing an accurate grouping proposal for each point. To the best of our knowledge, SGPN is the first framework to learn 3D instance-aware semantic segmentation on point clouds. Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results. We also demonstrate its flexibility by seamlessly incorporating 2D CNN features into the framework to boost performance

Stochastic Dynamics for Video Infilling

In this paper, we introduce a stochastic dynamics video infilling (SDVI) framework to generate frames between long intervals in a video. Our task differs from video interpolation which aims to produce transitional frames for a short interval between every two frames and increase the temporal resolution. Our task, namely video infilling, however, aims to infill long intervals with plausible frame sequences. Our framework models the infilling as a constrained stochastic generation process and sequentially samples dynamics from the inferred distribution. SDVI consists of two parts: (1) a bi-directional constraint propagation module to guarantee the spatial-temporal coherence among frames, (2) a stochastic sampling process to generate dynamics from the inferred distributions. Experimental results show that SDVI can generate clear frame sequences with varying contents. Moreover, motions in the generated sequence are realistic and able to transfer smoothly from the given start frame to the terminal frame. Our project site is https://xharlie.github.io/projects/project_sites/SDVI/video_results.htmlComment: Winter Conference on Applications of Computer Vision (WACV 2020

Service workers’ job performance: The roles of personality traits, organizational identification, and customer orientation

Organizational identification refers to employees’ perceived oneness and belongingness to their work organization, and has been argued to be associated with higher employee performance. This research aims to advance the literature by testing the boundary of this relationship with reference to a key construct in employee performance in the service domain: employee customer orientation. We collected data based on a sample of call center service workers. Employees rated their organizational identification, customer orientation, and personality traits. Supervisors independently rated their subordinates’ performance. Variables statistic tools were employed to analyse the data and test a series of hypotheses. We found that customer orientation strengthens the relationship between organizational identification and service workers’ job performance, and enhances the mediating effect of organizational identification on the relationship between service workers’ personality trait (i.e., agreeableness) and their performance. This research advances an argument that employee customer orientation moderates the relationship between employee organizational identification and employee job performance in the call center service provision domain. In addition, this is a pioneering study examining the roles of personality traits on employee organizational identification

Analysis of changes in large-scale circulation patterns driving extreme precipitation events over the central-eastern China

To an extent, large-scale circulation situations and moisture transport are responsible for extreme precipitation occurrence. The aim of our study is to investigate the possible modifications of circulation patterns (CPs) in driving extreme precipitation over the central-eastern China (CEC). The self-organizing map (SOM) and event synchronization methods are used to link the extreme precipitation events with CPs. Results show that 23% of rain gauges have a significant change point (at the 90% confidence level) in annual extreme precipitation from 1960 to 2015. Based on the identified change points, we classified the data into two periods, that is, 1960–1989 and 1990–2015. Overall, CPs characterized by obvious positive anomalies of 500 hPa geopotential height over the Eastern Eurasia continent and negative values over the surrounding oceans are highly synchronized with extreme precipitation events. During 1990–2015, the predominant CPs are more related to the extreme precipitation with enhanced event synchronization. We found that the CP changes produce an increase in extreme precipitation frequency from 1960–1989 to 1990–2015