SceneGraphVLM: Dynamic Scene Graph Generation from Video with Vision-Language Models
Scene graph generation provides a compact structured representation for visual perception, but accurate and fast graph prediction from images and videos remains challenging. Recent VLM-based methods can generate scene graphs end-to-end as structured text, yet often produce long outputs with irrelevant objects and relations. We present SceneGraphVLM, a compact method for image and video scene graph generation with small visual language models. SceneGraphVLM serializes graphs in a token-efficient TOON format and trains the model in two stages: supervised fine-tuning followed by reinforcement learning with hallucination-aware rewards that balance relation coverage and precision while penaliz...
| Authors | Vladislav Makarov, Mark Gizetdinov, Dmitry Yudin |
|---|---|
| Primary category | cs.CV |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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