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arXiv · 2604.17298

Frequency-guided Multi-level Reasoning for Scene Graph Generation in Video

Chenxing Li·Yiping Duan·Xiaoming Tao
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Abstract

Video Scene Graph Generation aims to obtain structured semantic representations of objects and their relationships in videos for high-level understanding. However, existing methods still have limitations in handling long-tail distributions. This paper proposes the Frequency-guided Relational Multi-level Reasoning (FReMuRe) model, which enhances the modeling ability of long-tail relationships from a mechanism perspective. We introduce relation-specific branches to deal gradient conflicts, yielding more balanced and tail-aware learning. And we design a frequency-aware dual-branch predicate embedding network to model high-frequency and low-frequency relationships separately and improve the recall rate of tail classes through gated fusion. Meanwhile, we propose two types of interchangeable relation classification heads: Bayesian Head for uncertainty estimation and new Gaussian Mixture Model Head to enhance intra-class diversity. Experimental results show that FReMuRe significantly improves the recall rate of long-tail relationships and overall reasoning robustness on the Action Genome dataset.

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Chenxing Li, Yiping Duan, Xiaoming Tao. 2026-04-19. Frequency-guided Multi-level Reasoning for Scene Graph Generation in Video. https://arxiv.org/abs/2604.17298

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