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arXiv · 2604.19631

MOSA: Motion-Guided Semantic Alignment for Dynamic Scene Graph Generation

Xuejiao Wang·Bohao Zhang·Changbo Wang·Gaoqi He
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Abstract

Dynamic Scene Graph Generation (DSGG) aims to structurally model objects and their dynamic interactions in video sequences for high-level semantic understanding. However, existing methods struggle with fine-grained relationship modeling, semantic representation utilization, and the ability to model tail relationships. To address these issues, this paper proposes a motion-guided semantic alignment method for DSGG (MoSA). First, a Motion Feature Extractor (MFE) encodes object-pair motion attributes such as distance, velocity, motion persistence, and directional consistency. Then, these motion attributes are fused with spatial relationship features through the Motion-guided Interaction Module (MIM) to generate motion-aware relationship representations. To further enhance semantic discrimination capabilities, the cross-modal Action Semantic Matching (ASM) mechanism aligns visual relationship features with text embeddings of relationship categories. Finally, a category-weighted loss strategy is introduced to emphasize learning of tail relationships. Extensive and rigorous testing shows that MoSA performs optimally on the Action Genome dataset.

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Xuejiao Wang, Bohao Zhang, Changbo Wang, Gaoqi He. 2026-04-21. MOSA: Motion-Guided Semantic Alignment for Dynamic Scene Graph Generation. https://arxiv.org/abs/2604.19631

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