分割
计算机科学
任务(项目管理)
人工智能
生成语法
语义学(计算机科学)
点(几何)
机器学习
模式识别(心理学)
训练集
零(语言学)
数学
语言学
哲学
几何学
程序设计语言
管理
经济
作者
Björn Michele,Alexandre Boulch,Gilles Puy,Maxime Bucher,Renaud Marlet
标识
DOI:10.1109/3dv53792.2021.00107
摘要
While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its application to 3D data is still recent and scarce, with just a few methods limited to classification. We present the first generative approach for both ZSL and Generalized ZSL (GZSL) on 3D data, that can handle both classification and, for the first time, semantic segmentation. We show that it reaches or outperforms the state of the art on ModelNet40 classification for both inductive ZSL and inductive GZSL. For semantic segmentation, we created three benchmarks for evaluating this new ZSL task, using S3DIS, ScanNet and SemanticKITTI. Our experiments show that our method outperforms strong baselines, which we additionally propose for this task.
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