SmoGVLM: A Small, Graph-enhanced Vision-Language Model
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2026
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| author | Mondal, Debjyoti Singh, Rituraj Panda, Subhadarshi |
| author_facet | Mondal, Debjyoti Singh, Rituraj Panda, Subhadarshi |
| contents | 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 structured knowledge augmentation for efficient, smaller-scale multimodal reasoning systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16517 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SmoGVLM: A Small, Graph-enhanced Vision-Language Model Mondal, Debjyoti Singh, Rituraj Panda, Subhadarshi Computer Vision and Pattern Recognition Computation and Language 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 structured knowledge augmentation for efficient, smaller-scale multimodal reasoning systems. |
| title | SmoGVLM: A Small, Graph-enhanced Vision-Language Model |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2604.16517 |