Video-based scene graph generation (VidSGG) is an approach that aims to represent video content in a dynamic graph by identifying visual entities and their relationships. Due to the inherently biased distribution and missing annotations in the training data, current VidSGG methods have been found to perform poorly on less-represented predicates. In this paper, we propose an explicit solution to address this under-explored issue by supplementing missing predicates that should be appear in the ground-truth annotations. Dubbed Trico, our method seeks to supplement the missing predicates by exploring three complementary spatio-temporal correlations. Guided by these correlations, the missing labels can be effectively supplemented thus achieving an unbiased predicate predictions. We validate the effectiveness of Trico on the most widely used VidSGG datasets, i.e., VidVRD and VidOR. Extensive experiments demonstrate the state-of-the-art performance achieved by Trico, particularly on those tail predicates.

我们提出了一种名为Trico的方法，通过探索三种互补的时空相关性来补充缺失的谓词，从而实现了无偏谓词预测，并验证了Trico在VidVRD和VidOR等广泛使用的VidSGG数据集上的卓越性能，特别是在那些尾部谓词上。

基于三元相关性引导的无偏视频场景图生成的标签补充