  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.
