Inverse Compositional Learning for Weakly-supervised Relation Grounding

Video relation grounding (VRG) is a significant and challenging problem in the domains of cross-modal learning and video understanding. In this study, we introduce a novel approach called inverse compositional learning (ICL) for weakly-supervised video relation grounding. Our approach represents relations at both the holistic and partial levels, formulating VRG as a joint optimization problem that encompasses reasoning at both levels. For holistic-level reasoning, we propose an inverse attention mechanism and a compositional encoder to generate compositional relevance features. Additionally, we introduce an inverse loss to evaluate and learn the relevance between visual features and relation features. At the partial-level reasoning, we introduce a grounding by classification scheme. By leveraging the learned holistic-level features and partial-level features, we train the entire model in an end-to-end manner. We conduct evaluations on two challenging datasets and demonstrate the substantial superiority of our proposed method over state-of-the-art methods. Extensive ablation studies confirm the effectiveness of our approach.