MARKER: Missing-Aware Low-Rank Expert Routing for Incomplete Multimodal Learning

Missing modalities commonly arise in real-world applications due to device failures, network instability, or privacy concerns. Recent studies address this issue with prompt-based fine-tuning. However, most of these prompt-based methods struggle with two aspects: (1) they rely on scenario-conditioned prompt sets and require explicit prompt selection for different modality-availability conditions, leading to a less unified adaptation pipeline; (2) most methods are modality-missing-aware but instance-agnostic, while a few recent approaches achieve instance-level adaptivity through retrieval-based mechanisms at the cost of additional components and increased system complexity. To address these issues, we propose MARKER, a Missing-Aware Low-Rank Expert Routing framework for incomplete multimodal learning. Specifically, we insert a LoRA-based mixture-of-experts module into a pretrained multimodal backbone. Trained on data with missing modalities, MAIL dynamically routes each input to a sparse combination of LoRA experts, conditioned on both the missingness pattern and instance-specific features, enabling adaptive prediction without maintaining per-scenario prompt banks. Importantly, this design decouples adaptation from per-pattern prompt specification, providing a unified mechanism across varying missingness conditions. We further conduct ablation studies on routing strategies and layer-wise expert-allocation shapes to assess their impact under different modality-missing scenarios. Extensive experiments on three real-world datasets show that MAIL consistently outperforms 11 strong baselines, demonstrating its effectiveness and robustness for incomplete multimodal learning.