On Robustness in Multimodal Learning

• URL: http://arxiv.org/abs/2304.04385v2
• Date: Tue, 11 Apr 2023 01:23:01 GMT
• Title: On Robustness in Multimodal Learning
• Authors: Brandon McKinzie, Joseph Cheng, Vaishaal Shankar, Yinfei Yang, Jonathon Shlens, Alexander Toshev
• Abstract summary: Multimodal learning is defined as learning over multiple input modalities such as video, audio, and text. We present a multimodal robustness framework to provide a systematic analysis of common multimodal representation learning methods.
• Score: 75.03719000820388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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