As point cloud provides a natural and flexible representation usable in
myriad applications (e.g., robotics and self-driving cars), the ability to
synthesize point clouds for analysis becomes crucial. Recently, Xie et al.
propose a generative model for unordered point sets in the form of an
energy-based model (EBM). Despite the model achieving an impressive performance
for point cloud generation, one separate model needs to be trained for each
category to capture the complex point set distributions. Besides, their method
is unable to classify point clouds directly and requires additional fine-tuning
for classification. One interesting question is: Can we train a single network
for a hybrid generative and discriminative model of point clouds? A similar
question has recently been answered in the affirmative for images, introducing
the framework of Joint Energy-based Model (JEM), which achieves high
performance in image classification and generation simultaneously. This paper
proposes GDPNet, the first hybrid Generative and Discriminative PointNet that
extends JEM for point cloud classification and generation. Our GDPNet retains
strong discriminative power of modern PointNet classifiers, while generating
point cloud samples rivaling state-of-the-art generative approaches.

该研究提出了 GDPNet，这是第一个综合生成和判别的 PointNet 模型，扩展了 JEM 用于点云分类和生成，既保留了现代 PointNet 分类器的强大判别能力，又生成了与最先进的生成方法相媲美的点云样本。

无序点集上混合生成判别式的 PointNet

A Hybrid Generative and Discriminative PointNet on Unordered Point Sets

Relation Extraction (RE) is to predict the relation type of two entities that
are mentioned in a piece of text, e.g., a sentence or a dialogue. When the
given text is long, it is challenging to identify indicative words for the
relation prediction. Recent advances on RE task are from BERT-based sequence
modeling and graph-based modeling of relationships among the tokens in the
sequence. In this paper, we propose to construct a latent multi-view graph to
capture various possible relationships among tokens. We then refine this graph
to select important words for relation prediction. Finally, the representation
of the refined graph and the BERT-based sequence representation are
concatenated for relation extraction. Specifically, in our proposed GDPNet
(Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph
Generator (GGG) to generate edges of the multi-view graph. The graph is then
refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we
show that GDPNet achieves the best performance on dialogue-level RE, and
comparable performance with the state-of-the-arts on sentence-level RE.

本研究提出了一种名为 GDPNet 的模型，该模型组合了多视图图形建模和动态时间规整池化技术，以提高关系抽取的性能。在对数句和对话级别的关系抽取任务中，GDPNet 取得了比以前更好或相当的表现。