Understanding 3D point cloud models for learning purposes has become an
imperative challenge for real-world identification such as autonomous driving
systems. A wide variety of solutions using deep learning have been proposed for
point cloud segmentation, object detection, and classification. These methods,
however, often require a considerable number of model parameters and are
computationally expensive. We study a semantic dimension of given 3D data
points and propose an efficient method called Meta-Semantic Learning
(Meta-SeL). Meta-SeL is an integrated framework that leverages two input 3D
local points (input 3D models and part-segmentation labels), providing a time
and cost-efficient, and precise projection model for a number of 3D recognition
tasks. The results indicate that Meta-SeL yields competitive performance in
comparison with other complex state-of-the-art work. Moreover, being random
shuffle invariant, Meta-SeL is resilient to translation as well as jittering
noise.

本文介绍了一种称为元语义学习 (Meta-SeL) 的方法，该方法利用两个输入的 3D 局部点（输入 3D 模型和部分分割标签），提供了一种时间和成本高效、精确的投影模型，用于许多 3D 识别任务。根据结果表明，Meta-SeL 在与其他复杂的最新研究的比较中，表现出了竞争性能。此外，Meta-SeL 对随机洗牌不变性具有弹性，也对平移和抖动噪声有较高的鲁棒性。

利用元语义学习进行 3D 模型 ShapeNet Core 分类

3D-model ShapeNet Core Classification using Meta-Semantic Learning

The opinion expressed in various Web sites and social-media is an essential
contributor to the decision making process of several organizations. Existing
sentiment analysis tools aim to extract the polarity (i.e., positive, negative,
neutral) from these opinionated contents. Despite the advance of the research
in the field, sentiment analysis tools give \textit{inconsistent} polarities,
which is harmful to business decisions. In this paper, we propose SentiQ, an
unsupervised Markov logic Network-based approach that injects the semantic
dimension in the tools through rules. It allows to detect and solve
inconsistencies and then improves the overall accuracy of the tools.
Preliminary experimental results demonstrate the usefulness of SentiQ.

本研究提出了 SentiQ，一种基于无监督 Markov 逻辑网络的方法来注入语义维度以检测并解决情感分析工具中的不一致性，从而提高准确性。初步实验结果证明了 SentiQ 的实用性。