MoIR mitigates modality dominance in VLMs by explicitly enriching low-information tokens with routed data from stronger modalities prior to LLM processing, yielding more balanced contributions and improved robustness under degradation.
Multimodal deep learning
2 Pith papers cite this work, alongside 9 external citations. Polarity classification is still indexing.
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The survey identifies a key tension in multilingual vision-language models between language neutrality via contrastive learning and cultural awareness via diverse data, with most benchmarks relying on translation-based evaluation.
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Information Router for Mitigating Modality Dominance in Vision-Language Models
MoIR mitigates modality dominance in VLMs by explicitly enriching low-information tokens with routed data from stronger modalities prior to LLM processing, yielding more balanced contributions and improved robustness under degradation.
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Multilingual Vision-Language Models, A Survey
The survey identifies a key tension in multilingual vision-language models between language neutrality via contrastive learning and cultural awareness via diverse data, with most benchmarks relying on translation-based evaluation.