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.
arXiv preprint arXiv:2504.17696 , year=
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MER-DG applies modality-entropy regularization to reduce fusion overfitting in multimodal domain generalization, reporting average gains of 5% over standard fusion and 2% over prior methods on EPIC-Kitchens and HAC benchmarks.
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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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MER-DG: Modality-Entropy Regularization for Multimodal Domain Generalization
MER-DG applies modality-entropy regularization to reduce fusion overfitting in multimodal domain generalization, reporting average gains of 5% over standard fusion and 2% over prior methods on EPIC-Kitchens and HAC benchmarks.