We address catastrophic forgetting issues in graph learning as incoming data
transits from one to another graph distribution. Whereas prior studies
primarily tackle one setting of graph continual learning such as incremental
node classification, we focus on a universal approach wherein each data point
in a task can be a node or a graph, and the task varies from node to graph
classification. We propose a novel method that enables graph neural networks to
excel in this universal setting. Our approach perseveres knowledge about past
tasks through a rehearsal mechanism that maintains local and global structure
consistency across the graphs. We benchmark our method against various
continual learning baselines in real-world graph datasets and achieve
significant improvement in average performance and forgetting across tasks.

我们提出了一种针对图学习中的灾难性遗忘问题的方法，在数据从一个图分布过渡到另一个图分布时，通过维持图的局部和全局结构一致性的排练机制来保持对过去任务的知识，并在实际图数据集中与各种持续学习基线进行了性能评测，取得了平均性能和任务遗忘方面的显著改进。

通用图连续学习

Universal Graph Continual Learning

Scene text detection has been made great progress in recent years. The
detection manners are evolving from axis-aligned rectangle to rotated rectangle
and further to quadrangle. However, current datasets contain very little curve
text, which can be widely observed in scene images such as signboard, product
name and so on. To raise the concerns of reading curve text in the wild, in
this paper, we construct a curve text dataset named CTW1500, which includes
over 10k text annotations in 1,500 images (1000 for training and 500 for
testing). Based on this dataset, we pioneering propose a polygon based curve
text detector (CTD) which can directly detect curve text without empirical
combination. Moreover, by seamlessly integrating the recurrent transverse and
longitudinal offset connection (TLOC), the proposed method can be end-to-end
trainable to learn the inherent connection among the position offsets. This
allows the CTD to explore context information instead of predicting points
independently, resulting in more smooth and accurate detection. We also propose
two simple but effective post-processing methods named non-polygon suppress
(NPS) and polygonal non-maximum suppression (PNMS) to further improve the
detection accuracy. Furthermore, the proposed approach in this paper is
designed in an universal manner, which can also be trained with rectangular or
quadrilateral bounding boxes without extra efforts. Experimental results on
CTW-1500 demonstrate our method with only a light backbone can outperform
state-of-the-art methods with a large margin. By evaluating only in the curve
or non-curve subset, the CTD + TLOC can still achieve the best results. Code is
available at this https URL

本文提出了一种基于多边形的弯曲文本检测器，结合循环横向和纵向偏移连接的无端到端可训练性，将非多边形抑制和多边形非最大抑制方法相结合以进一步提高检测准确性，此方法可以适用于矩形或四边形边界框的训练，并在 CTW-1500 上精度超过先前方法。