In this paper, we focus on semantic segmentation method for point clouds of
urban scenes. Our fundamental concept revolves around the collaborative
utilization of diverse scene representations to benefit from different context
information and network architectures. To this end, the proposed network
architecture, called APNet, is split into two branches: a point cloud branch
and an aerial image branch which input is generated from a point cloud. To
leverage the different properties of each branch, we employ a geometry-aware
fusion module that is learned to combine the results of each branch. Additional
separate losses for each branch avoid that one branch dominates the results,
ensure the best performance for each branch individually and explicitly define
the input domain of the fusion network assuring it only performs data fusion.
Our experiments demonstrate that the fusion output consistently outperforms the
individual network branches and that APNet achieves state-of-the-art
performance of 65.2 mIoU on the SensatUrban dataset. Upon acceptance, the
source code will be made accessible.

我们专注于城市场景点云的语义分割方法，提出了一种名为 APNet 的网络架构，利用不同上下文信息和网络架构的协同利用，将点云分支和航空影像分支进行几何感知融合，以达到数据融合的最佳性能。实验证明，融合模块的输出始终优于各个网络分支的结果，并且 APNet 在 SensatUrban 数据集上取得了 65.2 的 mIoU 的最新性能。

APNet: 都市级别航拍图像和点云的场景分割

APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds

The goal of zero-shot learning (ZSL) is to train a model to classify samples
of classes that were not seen during training. To address this challenging
task, most ZSL methods relate unseen test classes to seen(training) classes via
a pre-defined set of attributes that can describe all classes in the same
semantic space, so the knowledge learned on the training classes can be adapted
to unseen classes. In this paper, we aim to optimize the attribute space for
ZSL by training a propagation mechanism to refine the semantic attributes of
each class based on its neighbors and related classes on a graph of classes. We
show that the propagated attributes can produce classifiers for zero-shot
classes with significantly improved performance in different ZSL settings. The
graph of classes is usually free or very cheap to acquire such as WordNet or
ImageNet classes. When the graph is not provided, given pre-defined semantic
embeddings of the classes, we can learn a mechanism to generate the graph in an
end-to-end manner along with the propagation mechanism. However, this
graph-aided technique has not been well-explored in the literature. In this
paper, we introduce the attribute propagation network (APNet), which is
composed of 1) a graph propagation model generating attribute vector for each
class and 2) a parameterized nearest neighbor (NN) classifier categorizing an
image to the class with the nearest attribute vector to the image's embedding.
For better generalization over unseen classes, different from previous methods,
we adopt a meta-learning strategy to train the propagation mechanism and the
similarity metric for the NN classifier on multiple sub-graphs, each associated
with a classification task over a subset of training classes. In experiments
with two zero-shot learning settings and five benchmark datasets, APNet
achieves either compelling performance or new state-of-the-art results.

本文介绍了一种使用属性传播网络（APNet）进行零样本学习的方法，其优化了用于零样本分类的属性空间，并使用图传播模型和最近邻分类器来实现更好的分类准确性。