Shift Happens: Robustness and Reliability of Multimodal Foundation Models
Jianing Ni ⋅ Naman Goyal
Abstract
Multimodal models perform well on curated benchmarks but often fail unpredictably when deployed in the real world, where input distributions shift, modalities may be missing, and data quality varies. This workshop focuses on the fundamental ML challenge of robustness under distribution shift in multimodal settings: how do models degrade when visual or textual inputs shift? Can we predict and mitigate failures before deployment? What theoretical frameworks govern cross-modal robustness?
Speakers
Jianing Ni
AI safety and security
Naman Goyal
Video
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