Graph self-supervised learning (SSL) holds considerable promise for mining
and learning with graph-structured data. Yet, a significant challenge in graph
SSL lies in the feature discrepancy among graphs across different domains. In
this work, we aim to pretrain one graph neural network (GNN) on a varied
collection of graphs endowed with rich node features and subsequently apply the
pretrained GNN to unseen graphs. We present a general GraphAlign method that
can be seamlessly integrated into the existing graph SSL framework. To align
feature distributions across disparate graphs, GraphAlign designs alignment
strategies of feature encoding, normalization, alongside a
mixture-of-feature-expert module. Extensive experiments show that GraphAlign
empowers existing graph SSL frameworks to pretrain a unified and powerful GNN
across multiple graphs, showcasing performance superiority on both in-domain
and out-of-domain graphs.

通过对具有丰富节点特征的各类图进行预训练，并将预训练好的图神经网络应用于未见过的图，GraphAlign 方法有效地解决了图自监督学习中不同领域图之间特征差异的挑战，通过特征编码、规范化和特征专家模块的混合对齐策略实现了特征分布的统一，从而提升了在同领域和跨领域图上的性能优势。

GraphAlign：通过特征对齐在多个图上预训练一个图神经网络

GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via  Feature Alignment

LiDAR and cameras are complementary sensors for 3D object detection in
autonomous driving. However, it is challenging to explore the unnatural
interaction between point clouds and images, and the critical factor is how to
conduct feature alignment of heterogeneous modalities. Currently, many methods
achieve feature alignment by projection calibration only, without considering
the problem of coordinate conversion accuracy errors between sensors, leading
to sub-optimal performance. In this paper, we present GraphAlign, a more
accurate feature alignment strategy for 3D object detection by graph matching.
Specifically, we fuse image features from a semantic segmentation encoder in
the image branch and point cloud features from a 3D Sparse CNN in the LiDAR
branch. To save computation, we construct the nearest neighbor relationship by
calculating Euclidean distance within the subspaces that are divided into the
point cloud features. Through the projection calibration between the image and
point cloud, we project the nearest neighbors of point cloud features onto the
image features. Then by matching the nearest neighbors with a single point
cloud to multiple images, we search for a more appropriate feature alignment.
In addition, we provide a self-attention module to enhance the weights of
significant relations to fine-tune the feature alignment between heterogeneous
modalities. Extensive experiments on nuScenes benchmark demonstrate the
effectiveness and efficiency of our GraphAlign.

本文介绍了一种名为 GraphAlign 的更准确的 3D 物体检测特征对齐策略，采用图匹配的方式融合来自图像分割编码器的图像特征和来自 LiDAR 的点云特征，通过投影校准和自我注意机制实现异构模态间的特征对齐。我们在 nuScenes 基准上进行广泛实验，证明了 GraphAlign 的有效性和效率。