MuLTI: Efficient Video-and-Language Understanding with MultiWay-Sampler and Multiple Choice Modeling
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Video-and-language understanding has a variety of applications in the industry, such as video question answering, text-video retrieval and multi-label classification. Existing video-and-language understanding methods generally adopt heavy multi-modal encoders and feature fusion modules, which consume large amounts of GPU memory. Especially, they have difficulty dealing with dense video frames or long text that are prevalent in industrial applications. In this paper, we propose MuLTI, a highly accurate and memory-efficient video-and-language understanding model that achieves efficient and effective feature fusion through feature sampling and attention modules. Therefore, MuLTI can handle longer sequences with limited GPU memory. Then, we introduce an attention-based adapter to the encoders, which finetunes the shallow features to improve the model's performance with low GPU memory consumption. Finally, to further improve the model's performance, we introduce a new pretraining task named Multiple Choice Modeling to bridge the task gap between pretraining and downstream tasks and enhance the model's ability to align the video and the text. Benefiting from the efficient feature fusion module, the attention-based adapter and the new pretraining task, MuLTI achieves state-of-the-art performance on multiple datasets. Implementation and pretrained models will be released.
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