SmoGVLM: A Small, Graph-enhanced Vision-Language Model
Large vision-language models (VLMs) achieve strong performance on multimodal tasks but often suffer from hallucination and poor grounding in knowledge-intensive reasoning. We propose SmoGVLM, a small, graph-enhanced VLM that integrates structured knowledge with visual and textual modalities, using Graph Neural Networks. We investigate the effects of our method across a range of model sizes, from tiny (1.3B) to large (13B) models. Our results demonstrate that, when trained using our approach, a small model can achieve performance gains upto 16.24%, and surpass its larger counterparts, outperforming larger VLMs and strong fine-tuned baselines. These results highlight the potential of struct...
| Authors | Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda |
|---|---|
| Primary category | cs.CV |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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