Dynamic Modality Scheduling for Multimodal Large Models via Confidence, Uncertainty, and Semantic Consistency

• URL: http://arxiv.org/abs/2506.12724v1
• Date: Sun, 15 Jun 2025 05:15:52 GMT
• Title: Dynamic Modality Scheduling for Multimodal Large Models via Confidence, Uncertainty, and Semantic Consistency
• Authors: Hiroshi Tanaka, Anika Rao, Hana Satou, Michael Johnson, Sofia García,
• Abstract summary: We propose Dynamic Modality Scheduling (DMS), a novel framework that adaptively adjusts the contribution of each modality at a per-sample level.<n> Experimental results on VQA, image-text retrieval, and captioning tasks show that DMS significantly improves both clean and robust performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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