Video Question Answering with Iterative Video-Text Co-Tokenization

• URL: http://arxiv.org/abs/2208.00934v1
• Date: Mon, 1 Aug 2022 15:35:38 GMT
• Title: Video Question Answering with Iterative Video-Text Co-Tokenization
• Authors: AJ Piergiovanni and Kairo Morton and Weicheng Kuo and Michael S. Ryoo and Anelia Angelova
• Abstract summary: We propose a novel multi-stream video encoder for video question answering. We experimentally evaluate the model on several datasets, such as MSRVTT-QA, MSVD-QA, IVQA. Our model reduces the required GFLOPs from 150-360 to only 67, producing a highly efficient video question answering model.
• Score: 77.66445727743508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video question answering is a challenging task that requires understanding jointly the language input, the visual information in individual video frames, as well as the temporal information about the events occurring in the video. In this paper, we propose a novel multi-stream video encoder for video question answering that uses multiple video inputs and a new video-text iterative co-tokenization approach to answer a variety of questions related to videos. We experimentally evaluate the model on several datasets, such as MSRVTT-QA, MSVD-QA, IVQA, outperforming the previous state-of-the-art by large margins. Simultaneously, our model reduces the required GFLOPs from 150-360 to only 67, producing a highly efficient video question answering model.

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