BriefGPT.xyz
Jun, 2023
可解释的自监督变压器深入探究点云
A deep dive into explainable self-supervised transformers for point clouds
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Ioannis Romanelis, Vlassis Fotis, Konstantinos Moustakas, Adrian Munteanu
TL;DR
本文研究了transformers在点云领域中通过自监督学习所获得的特性，探讨了其预训练方案的有效性，分析了数据数量对网络特征的影响，提出了一种解冻策略，并在分类任务中取得了最优结果。
Abstract
In this paper we delve into the properties of
transformers
, attained through
self-supervision
, in the point cloud domain. Specifically, we evaluate the effectiveness of
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