Visual Representation Alignment for Multimodal Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915543116677120 |
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| author | Yoon, Heeji Jung, Jaewoo Kim, Junwan Choi, Hyungyu Shin, Heeseong Lim, Sangbeom An, Honggyu Kim, Chaehyun Han, Jisang Kim, Donghyun Eom, Chanho Hong, Sunghwan Kim, Seungryong |
| author_facet | Yoon, Heeji Jung, Jaewoo Kim, Junwan Choi, Hyungyu Shin, Heeseong Lim, Sangbeom An, Honggyu Kim, Chaehyun Han, Jisang Kim, Donghyun Eom, Chanho Hong, Sunghwan Kim, Seungryong |
| contents | Multimodal large language models (MLLMs) trained with visual instruction tuning have achieved strong performance across diverse tasks, yet they remain limited in vision-centric tasks such as object counting or spatial reasoning. We attribute this gap to the prevailing text-only supervision paradigm, which provides only indirect guidance for the visual pathway and often leads MLLMs to discard fine-grained visual details during training. In this paper, we present VIsual Representation ALignment (VIRAL), a simple yet effective regularization strategy that aligns the internal visual representations of MLLMs with those of pre-trained vision foundation models (VFMs). By explicitly enforcing this alignment, VIRAL enables the model not only to retain critical visual details from the input vision encoder but also to complement additional visual knowledge from VFMs, thereby enhancing its ability to reason over complex visual inputs. Our experiments demonstrate consistent improvements across all tasks on widely adopted multimodal benchmarks. Furthermore, we conduct comprehensive ablation studies to validate the key design choices underlying our framework. We believe this simple finding opens up an important direction for the effective integration of visual information in training MLLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visual Representation Alignment for Multimodal Large Language Models Yoon, Heeji Jung, Jaewoo Kim, Junwan Choi, Hyungyu Shin, Heeseong Lim, Sangbeom An, Honggyu Kim, Chaehyun Han, Jisang Kim, Donghyun Eom, Chanho Hong, Sunghwan Kim, Seungryong Computer Vision and Pattern Recognition Multimodal large language models (MLLMs) trained with visual instruction tuning have achieved strong performance across diverse tasks, yet they remain limited in vision-centric tasks such as object counting or spatial reasoning. We attribute this gap to the prevailing text-only supervision paradigm, which provides only indirect guidance for the visual pathway and often leads MLLMs to discard fine-grained visual details during training. In this paper, we present VIsual Representation ALignment (VIRAL), a simple yet effective regularization strategy that aligns the internal visual representations of MLLMs with those of pre-trained vision foundation models (VFMs). By explicitly enforcing this alignment, VIRAL enables the model not only to retain critical visual details from the input vision encoder but also to complement additional visual knowledge from VFMs, thereby enhancing its ability to reason over complex visual inputs. Our experiments demonstrate consistent improvements across all tasks on widely adopted multimodal benchmarks. Furthermore, we conduct comprehensive ablation studies to validate the key design choices underlying our framework. We believe this simple finding opens up an important direction for the effective integration of visual information in training MLLMs. |
| title | Visual Representation Alignment for Multimodal Large Language Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.07979 |