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Poster
Robustness in Multimodal Learning under Train-Test Modality Mismatch
Brandon McKinzie · Vaishaal Shankar · Joseph Cheng · Yinfei Yang · Jonathon Shlens · Alexander Toshev

Thu Jul 27 01:30 PM -- 03:00 PM (PDT) @ Exhibit Hall 1 #717
in Poster Session 5 »
Multimodal learning is defined as learning over multiple heterogeneous input modalities such as video, audio, and text. In this work, we are concerned with understanding how models behave as the type of modalities differ between training and deployment, a situation that naturally arises in many applications of multimodal learning to hardware platforms. We present a multimodal robustness framework to provide a systematic analysis of common multimodal representation learning methods. Further, we identify robustness short-comings of these approaches and propose two intervention techniques leading to $1.5\times$-$4\times$ robustness improvements on three datasets, AudioSet, Kinetics-400 and ImageNet-Captions. Finally, we demonstrate that these interventions better utilize additional modalities, if present, to achieve competitive results of $44.2$ mAP on AudioSet 20K.
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Author Information

Brandon McKinzie (Apple)
Vaishaal Shankar (Amazon)
Joseph Cheng (Humane)
Yinfei Yang (Apple)
Jonathon Shlens (Google)
Alexander Toshev (Apple ML Research)

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