The Development of NBA in China: A Glocalization Perspective

The growing sport industry and 1.3 billion potential consumers in China have been garnering tremendous attention from more and more overseas professional sport leagues. Comparatively, the National Basketball Association (NBA) has had remarkable success in the Chinese market. From the perspective of sport competition or marketing operations, the NBA’s achievement in China provides a model for other overseas sport leagues. This case study was organized by summarizing the developmental history of NBA in China, analyzing its current promotional practices, investigating into its marketing strategies, and extrapolating practical references for other sport leagues aiming to penetrating into the Chinese marketplace. In the perspective of glocalization, multinational corporations should combine both standardized and adapted elements to conceptualize globally and act locally (Tanahashi, 2008). By taking this approach, marketers can meet the needs of local consumers effectively while still maintaining some extent of global standardization (Singh, Kumar, & Baack, 2005). To obtain in-depth understanding about NBA globalization and localization in China, we conducted one-on-one interviews with Chinese academic scholars in sport management and practitioners in Chinese basketball industry and NBA China. Two focus groups with six participants in each group were conducted to learn the perception of NBA products from the perspective of Chinese consumer. The qualitative data analysis was organized around four major aspects: products, media, management and public relations, which were highlighted in the glocalization of transnational corporations (Yang, 2003; Zhang, 2007) The current case study concluded that although NBA has achieved huge successes in the areas of building a large fan base, increasing media exposure, and garnering net income after its entry to China, it still faces many challenges. One viable solution for the NBA is to bring authentic American cultural commodities while adding Chinese characteristics to accommodate local fans. Combining global heroes such as Michael Jordan and Kobe Bryant and local hero such as Yao Ming, Yi Jianlian, and Jeremy Lin, NBA games will continue to appeal to millions of Chinese fans. Meantime, NBA management needs to continue seeking ways to work out and through the differences in government models and cultural contexts between China and United States. Some viable actions include the promotion of Chinese youth basketball, the training service for elite basketball players, and government-level public relations. In addition, this study suggested that the research framework of glocalization would be an ever intriguing inquiry needed for other sport organizations or leagues seeking expansion to overseas markets

Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection

LiDAR-based 3D Object detectors have achieved impressive performances in many benchmarks, however, multisensors fusion-based techniques are promising to further improve the results. PointPainting, as a recently proposed framework, can add the semantic information from the 2D image into the 3D LiDAR point by the painting operation to boost the detection performance. However, due to the limited resolution of 2D feature maps, severe boundary-blurring effect happens during re-projection of 2D semantic segmentation into the 3D point clouds. To well handle this limitation, a general multimodal fusion framework MSF has been proposed to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, MSF includes three main modules. First, SOTA off-the-shelf 2D/3D semantic segmentation approaches are employed to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further re-projected into the 3D point clouds with calibrated parameters. To handle the misalignment between the 2D and 3D parsing results, an AAF module is proposed to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a DFF module to aggregate deep features in different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets new SOTA results on the nuScenes testing benchmark.Comment: Submitted to T-ITS Journa

Reducing Crosstalk of Silicon-based Optical Switch with All-optical Multi-wavelength Regenerator

Improving crosstalk performance of Mach–Zehnder-interferometer-type optical switches is experimentally investigated by use of an all-optical multi-wavelength regenerator. Extinction ratio and bit error rate of WDM signals are simultaneously improved in proposed regenerative optical switching

ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection

Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination. Scene-level methods tend to lose local details that are crucial for recognizing the road objects, while point/voxel-level methods inherently suffer from limited receptive field that is incapable of perceiving large objects or context environments. Considering region-level representations are more suitable for 3D object detection, we devise a new unsupervised point cloud pre-training framework, called ProposalContrast, that learns robust 3D representations by contrasting region proposals. Specifically, with an exhaustive set of region proposals sampled from each point cloud, geometric point relations within each proposal are modeled for creating expressive proposal representations. To better accommodate 3D detection properties, ProposalContrast optimizes with both inter-cluster and inter-proposal separation, i.e., sharpening the discriminativeness of proposal representations across semantic classes and object instances. The generalizability and transferability of ProposalContrast are verified on various 3D detectors (i.e., PV-RCNN, CenterPoint, PointPillars and PointRCNN) and datasets (i.e., KITTI, Waymo and ONCE).Comment: Accepted to ECCV 2022. Code: https://github.com/yinjunbo/ProposalContras