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Video-based scene graph generation (VidSGG) is an approach that aims torepresent video content in a dynamic graph by identifying visual entities andtheir relationships. Due to the inherently biased distribution and missingannotations in the training data, current VidSGG methods have been found toperform poorly on less-represented predicates. In this paper, we propose anexplicit solution to address this under-explored issue by supplementing missingpredicates that should be appear in the ground-truth annotations. Dubbed Trico,our method seeks to supplement the missing predicates by exploring threecomplementary spatio-temporal correlations. Guided by these correlations, themissing labels can be effectively supplemented thus achieving an unbiasedpredicate predictions. We validate the effectiveness of Trico on the mostwidely used VidSGG datasets, i.e., VidVRD and VidOR. Extensive experimentsdemonstrate the state-of-the-art performance achieved by Trico, particularly onthose tail predicates.

Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation