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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.12724 |
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| _version_ | 1866912430229028864 |
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| author | Tanaka, Hiroshi Rao, Anika Satou, Hana Johnson, Michael García, Sofia |
| author_facet | Tanaka, Hiroshi Rao, Anika Satou, Hana Johnson, Michael García, Sofia |
| contents | Multimodal Large Models (MLLMs) have achieved remarkable progress in vision-language understanding and generation tasks. However, existing MLLMs typically rely on static modality fusion strategies, which treat all modalities equally regardless of their instance-level reliability or semantic contribution. This often leads to suboptimal performance, especially in scenarios with noisy, missing, or misaligned modalities.
In this paper, we propose Dynamic Modality Scheduling (DMS), a novel framework that adaptively adjusts the contribution of each modality at a per-sample level. DMS evaluates each modality based on three key factors: (1) \textit{confidence}, estimated from predictive entropy; (2) \textit{uncertainty}, obtained via Monte Carlo dropout; and (3) \textit{semantic consistency}, computed through inter-modal similarity. These signals are combined through a learnable or rule-based scheduler to generate soft modality weights used in downstream fusion.To ensure stable training, we further introduce a \textit{Modality Weight Consistency Loss}, which regularizes the fused representation to stay close to unimodal embeddings proportionally to their assigned weights. Our method is model-agnostic and can be integrated into existing MLLMs such as BLIP-2 and LLaVA. Experimental results on VQA, image-text retrieval, and captioning tasks show that DMS significantly improves both clean and robust performance, especially under modality corruption or dropout conditions. This work provides a general and effective mechanism to enable instance-aware and robustness-enhanced multimodal modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12724 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Modality Scheduling for Multimodal Large Models via Confidence, Uncertainty, and Semantic Consistency Tanaka, Hiroshi Rao, Anika Satou, Hana Johnson, Michael García, Sofia Computer Vision and Pattern Recognition Multimodal Large Models (MLLMs) have achieved remarkable progress in vision-language understanding and generation tasks. However, existing MLLMs typically rely on static modality fusion strategies, which treat all modalities equally regardless of their instance-level reliability or semantic contribution. This often leads to suboptimal performance, especially in scenarios with noisy, missing, or misaligned modalities. In this paper, we propose Dynamic Modality Scheduling (DMS), a novel framework that adaptively adjusts the contribution of each modality at a per-sample level. DMS evaluates each modality based on three key factors: (1) \textit{confidence}, estimated from predictive entropy; (2) \textit{uncertainty}, obtained via Monte Carlo dropout; and (3) \textit{semantic consistency}, computed through inter-modal similarity. These signals are combined through a learnable or rule-based scheduler to generate soft modality weights used in downstream fusion.To ensure stable training, we further introduce a \textit{Modality Weight Consistency Loss}, which regularizes the fused representation to stay close to unimodal embeddings proportionally to their assigned weights. Our method is model-agnostic and can be integrated into existing MLLMs such as BLIP-2 and LLaVA. Experimental results on VQA, image-text retrieval, and captioning tasks show that DMS significantly improves both clean and robust performance, especially under modality corruption or dropout conditions. This work provides a general and effective mechanism to enable instance-aware and robustness-enhanced multimodal modeling. |
| title | Dynamic Modality Scheduling for Multimodal Large Models via Confidence, Uncertainty, and Semantic Consistency |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.12724 |