Taking a Closer Look At Visual Relation: Unbiased Video Scene Graph Generation With Decoupled Label Learning

Current video-based scene graph generation (VidSGG) methods have been found to perform poorly in predicting predicates that are less represented due to the inherently biased distribution of the training data. In this paper, we take a closer look at the inherent characteristics of predicates and identify that most visual relations (e.g.
sit_above
) involve both actional pattern (
sit
) and spatial pattern (
above
), while the distribution bias is much less severe at the pattern level. Based on this insight, we propose a decoupled label learning (DLL) paradigm to address the intractable visual relation prediction from the pattern-level perspective. Specifically, DLL decouples the predicate labels and adopts separate classifiers to learn actional and spatial patterns respectively. The patterns are then combined and mapped back to the predicate. Moreover, we propose a knowledge-level label decoupling method to transfer non-target knowledge from head predicates to tail predicates within the same pattern to calibrate the distribution of tail classes. We validate the effectiveness of DLL on the commonly used VidSGG benchmark, i.e. VidVRD. Extensive experiments demonstrate that the DLL offers a remarkably simple but highly effective solution to the long-tailed problem, achieving the state-of-the-art VidSGG performance.