We introduce a pioneering approach to self-supervised learning for point
clouds, employing a geometrically informed mask selection strategy called
GeoMask3D (GM3D) to boost the efficiency of Masked Auto Encoders (MAE). Unlike
the conventional method of random masking, our technique utilizes a
teacher-student model to focus on intricate areas within the data, guiding the
model's focus toward regions with higher geometric complexity. This strategy is
grounded in the hypothesis that concentrating on harder patches yields a more
robust feature representation, as evidenced by the improved performance on
downstream tasks. Our method also presents a complete-to-partial feature-level
knowledge distillation technique designed to guide the prediction of geometric
complexity utilizing a comprehensive context from feature-level information.
Extensive experiments confirm our method's superiority over State-Of-The-Art
(SOTA) baselines, demonstrating marked improvements in classification, and
few-shot tasks.

我们引入了一种自监督学习的创新方法，采用一种名为 GeoMask3D（GM3D）的几何信息驱动的蒙版选择策略，以提高蒙版自编码器（MAE）的效率。与传统的随机蒙版方法不同，我们的技术利用师生模型来专注于数据中的复杂区域，引导模型关注具有更高几何复杂性的区域。我们的方法还提供了一种基于特征级信息的完全到部分的特征级知识提取技术，以指导几何复杂性的预测。大量实验证实了我们方法在分类和少样本任务中相对于现有基准的卓越性能。

GeoMask3D：3D 自监督点云学习的几何信息掩码选择

GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised  Point Cloud Learning in 3D

We study the new task of class-incremental Novel Class Discovery
(class-iNCD), which refers to the problem of discovering novel categories in an
unlabelled data set by leveraging a pre-trained model that has been trained on
a labelled data set containing disjoint yet related categories. Apart from
discovering novel classes, we also aim at preserving the ability of the model
to recognize previously seen base categories. Inspired by rehearsal-based
incremental learning methods, in this paper we propose a novel approach for
class-iNCD which prevents forgetting of past information about the base classes
by jointly exploiting base class feature prototypes and feature-level knowledge
distillation. We also propose a self-training clustering strategy that
simultaneously clusters novel categories and trains a joint classifier for both
the base and novel classes. This makes our method able to operate in a
class-incremental setting. Our experiments, conducted on three common
benchmarks, demonstrate that our method significantly outperforms
state-of-the-art approaches. Code is available at
this https URL

本文探讨了一种新的任务，即类增量的新类别发现（class-iNCD），提出了一种基于预训练模型的方法，利用基类特征原型和特征级别的知识蒸馏，结合自我训练聚类策略实现发现新类别，且能够识别先前已见基类别，实验结果表明该方法显著优于现有方法。