MULTIMODAL MASKED AUTOENCODERS LEARN TRANSFERABLE REPRESENTATIONS

Abstract

Building scalable models to learn from diverse, multimodal data remains an open challenge. For vision-language data, the dominant approaches are based on contrastive learning objectives that train a separate encoder for each modality. While effective, contrastive learning approaches introduce sampling bias depending on the data augmentations used, which can degrade performance on downstream tasks. Moreover, these methods are limited to paired image-text data, and cannot leverage widely-available unpaired data. In this paper, we investigate whether a large multimodal model trained purely via masked token prediction, without using modality-specific encoders or contrastive learning, can learn transferable representations for downstream tasks. We propose a simple and scalable network architecture, the Multimodal Masked Autoencoder (M3AE), which learns a unified encoder for both vision and language data via masked token prediction. We provide an empirical study of M3AE trained on a large-scale image-text dataset, and find that M3AE is able to learn generalizable representations that transfer well to downstream tasks. Surprisingly, we find that M3AE benefits from a higher text mask ratio (50-90%), in contrast to BERT whose standard masking ratio is 15%, due to the joint training of two data modalities. We also provide qualitative analysis showing that the learned representation incorporates meaningful information from both image and language. Lastly, we demonstrate the scalability of M3AE with larger model size and training time, and its flexibility to train on both paired image-text data as well as unpaired data.

1. INTRODUCTION

With the rapid advances in neural architectures (Vaswani et al., 2017) and hardware performance, self-supervised pre-training has made tremendous progress in natural language processing (NLP) and vision (He et al., 2021; Devlin et al., 2018; Bao et al., 2021; Brown et al., 2020) . The underlying idea, often referred as masked token prediction, is conceptually simple: the model learns to predict a removed portion of the data. Masked token prediction has enabled highly successful methods for pre-training in NLP and vision, including Transformer (Vaswani et al., 2017) , GPT (Brown et al., 2020) , BERT (Devlin et al., 2018), and MAE (He et al., 2021) . These pre-trained representations have been shown to generalize well to various downstream tasks. The cornerstone of these successes is that these methods excellently leverage large and diverse datasets. Indeed, with the scaling up of data diversity and model capacity, there is still no sign of plateau on generalization to various downstream tasks (Devlin et al., 2018; He et al., 2021) . Driven by the successes in NLP and vision, there has been significant interest in improving visual representation learning by training on large and diverse multimodal datasets that contains both images and text. These datasets, such as CC12M (Changpinyo et al., 2021) and YFCC100M (Thomee et al., 2015) , are often much more scalable than explicitly labeled datasets such as ImageNet (Deng et al., 2009) , and the diverse language data can provide rich supervision to train more generalizable representations. The dominant paradigm for multimodal pre-training is cross-modal contrastive learning, such as CLIP (Radford et al., 2021) and ALIGN (Jia et al., 2021) . These methods show that cross-modal contrastive learning models, trained on giant corpora of paired image-and-text, can generalize well to various downstream tasks. Despite these progresses, a major limitation for contrastive learning is that 1

