Papers › Video Question Answering with Iterative Video-Text Co-Tokenization

Video Question Answering with Iterative Video-Text Co-Tokenization

1 Aug 2022arXiv:2208.00934archive 2025-07-28

AJ Piergiovanni, Kairo Morton, Weicheng Kuo, Michael S. Ryoo, Anelia Angelova

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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Tasks

Question AnsweringVideo Question AnsweringVisual Question Answering (VQA)

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Video Question Answering iVQA Co-Tokenization Accuracy 38.2 #4 of 7 Archive leaderboard report
Visual Question Answering (VQA) MSRVTT-QA Co-Tokenization Accuracy .457 #14 of 34 Archive leaderboard report
Visual Question Answering (VQA) MSVD-QA Co-Tokenization Accuracy .486 #23 of 36 Archive leaderboard report

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