• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops >SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding
【24h】

SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding

机译：Skelneton 2019：数据集和深度学习对几何形状理解的挑战

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shape Understanding workshop to utilize existing and develop novel deep learning architectures for shape understanding. We observed that unlike traditional segmentation and detection tasks, geometry understanding is still a new area for deep learning techniques. SkelNetOn aims to bring together researchers from different domains to foster learning methods on global shape understanding tasks. We aim to improve and evaluate the state-of-the-art shape understanding approaches, and to serve as reference benchmarks for future research. Similar to other challenges in computer vision, SkelNetOn proposes three datasets and corresponding evaluation methodologies; all coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2019 conference. In this paper, we describe and analyze characteristics of datasets, define the evaluation criteria of the public competitions, and provide baselines for each task.
机译：我们展示了Skelneton 2019挑战和深入学习几何形状理解研讨会利用现有和开发新型深度学习架构的形状理解。我们观察到，与传统的细分和检测任务不同，几何理解仍然是深度学习技术的新领域。 Skelneton旨在将来自不同领域的研究人员汇集在一起​​，促进全球造型理解任务的学习方法。我们的目标是改进和评估最先进的形状理解方法，并作为未来研究的参考基准。类似于计算机视觉中的其他挑战，Skelneton提出了三个数据集和相应的评估方法;所有与CVPR 2019年会议共处的专用车间都连贯地捆绑在三场比赛中。在本文中，我们描述并分析了数据集的特征，定义了公共竞争的评估标准，并为每项任务提供基线。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号