We investigate transductive zero-shot point cloud semantic segmentation in
this paper, where unseen class labels are unavailable during training.
Actually, the 3D geometric elements are essential cues to reason the 3D object
type. If two categories share similar geometric primitives, they also have
similar semantic representations. Based on this consideration, we propose a
novel framework to learn the geometric primitives shared in seen and unseen
categories' objects, where the learned geometric primitives are served for
transferring knowledge from seen to unseen categories. Specifically, a group of
learnable prototypes automatically encode geometric primitives via
back-propagation. Then, the point visual representation is formulated as the
similarity vector of its feature to the prototypes, which implies semantic cues
for both seen and unseen categories. Besides, considering a 3D object composed
of multiple geometric primitives, we formulate the semantic representation as a
mixture-distributed embedding for the fine-grained match of visual
representation. In the end, to effectively learn the geometric primitives and
alleviate the misclassification issue, we propose a novel unknown-aware infoNCE
loss to align the visual and semantic representation. As a result, guided by
semantic representations, the network recognizes the novel object represented
with geometric primitives. Extensive experiments show that our method
significantly outperforms other state-of-the-art methods in the harmonic
mean-intersection-over-union (hIoU), with the improvement of 17.8%, 30.4% and
9.2% on S3DIS, ScanNet and SemanticKITTI datasets, respectively. Codes will be
released.

本文介绍了一种用于零样本点云语义分割的新型框架，该框架利用了几何基元以在训练期间进行类别之间的知识转移，并使用未知感知信息熵来校准视觉与语义表示。通过大量实验，该方法在 S3DIS、ScanNet 和 SemanticKITTI 数据集上的 hIoU 指标得到了显著提升。

通过传递几何基元实现零样本点云分割

Zero-shot Point Cloud Segmentation by Transferring Geometric Primitives

Many existing approaches for 3D point cloud semantic segmentation are fully
supervised. These fully supervised approaches heavily rely on large amounts of
labeled training data that are difficult to obtain and cannot segment new
classes after training. To mitigate these limitations, we propose a novel
attention-aware multi-prototype transductive few-shot point cloud semantic
segmentation method to segment new classes given a few labeled examples.
Specifically, each class is represented by multiple prototypes to model the
complex data distribution of labeled points. Subsequently, we employ a
transductive label propagation method to exploit the affinities between labeled
multi-prototypes and unlabeled points, and among the unlabeled points.
Furthermore, we design an attention-aware multi-level feature learning network
to learn the discriminative features that capture the geometric dependencies
and semantic correlations between points. Our proposed method shows significant
and consistent improvements compared to baselines in different few-shot point
cloud semantic segmentation settings (i.e., 2/3-way 1/5-shot) on two benchmark
datasets. Our code is available at this https URL

为了解决 3D 点云语义分割中数据有限和模型泛化性能差的问题，本文提出了一种基于注意力机制多突出物体的跨导语义分割方法，并在各种 Few-shot 点云语义分割场景下取得了显著的效果提升。

少样本 3D 点云语义分割

Few-shot 3D Point Cloud Semantic Segmentation

In this paper, we propose a novel framework, called Semi-supervised Embedding
in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector
representation that systematically captures the topological proximity,
attribute affinity and label similarity of vertices in a partially labeled
attributed network (PLAN). Our method is designed to work in both transductive
and inductive settings while explicitly alleviating noise effects from
outliers. Experimental results on various datasets drawn from the web, text and
image domains demonstrate the advantages of SEANO over state-of-the-art methods
in semi-supervised classification under transductive as well as inductive
settings. We also show that a subset of parameters in SEANO is interpretable as
outlier score and can significantly outperform baseline methods when applied
for detecting network outliers. Finally, we present the use of SEANO in a
challenging real-world setting -- flood mapping of satellite images and show
that it is able to outperform modern remote sensing algorithms for this task.

本文提出了一种名为 SEANO 的新框架，用于学习部分标记属性网络 (PLAN) 中顶点的拓扑接近性、属性亲和力和标签相似性的低维向量表示，并在消除异常值的同时工作于传递和归纳设置，并证明在各种数据集上都显著优于现有方法。

带有离群节点的属性网络的半监督嵌入

Semi-supervised Embedding in Attributed Networks with Outliers

We present a semi-supervised learning framework based on graph embeddings.
Given a graph between instances, we train an embedding for each instance to
jointly predict the class label and the neighborhood context in the graph. We
develop both transductive and inductive variants of our method. In the
transductive variant of our method, the class labels are determined by both the
learned embeddings and input feature vectors, while in the inductive variant,
the embeddings are defined as a parametric function of the feature vectors, so
predictions can be made on instances not seen during training. On a large and
diverse set of benchmark tasks, including text classification, distantly
supervised entity extraction, and entity classification, we show improved
performance over many of the existing models.

介绍了一种基于图嵌入的半监督学习框架，重点研究了在图中联合预测类别标签和邻域上下文信息的方法，并在包括文本分类等多个基准任务上，相比已有的模型表现出了更好的性能。