Scene graph generation (SGG) involves analyzing images to extract meaningful
information about objects and their relationships. Given the dynamic nature of
the visual world, it becomes crucial for AI systems to detect new objects and
establish their new relationships with existing objects. To address the lack of
continual learning methodologies in SGG, we introduce the comprehensive
Continual ScenE Graph Generation (CSEGG) dataset along with 3 learning
scenarios and 8 evaluation metrics. Our research investigates the continual
learning performances of existing SGG methods on the retention of previous
object entities and relationships as they learn new ones. Moreover, we also
explore how continual object detection enhances generalization in classifying
known relationships on unknown objects. We conduct extensive experiments
benchmarking and analyzing the classical two-stage SGG methods and the most
recent transformer-based SGG methods in continual learning settings, and gain
valuable insights into the CSEGG problem. We invite the research community to
explore this emerging field of study.

通过引入包含三个学习场景和八个评估指标的综合连续场景图生成（CSEGG）数据集，研究深入探索现有的 SGG 方法在学习新对象时对先前对象实体和关系的保留情况，以及连续目标检测如何增强对未知对象上已知关系的泛化性能。