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.

本文提出了一种新型的多流视频编码器，使用多个视频输入和新的视频文本迭代共同标记方法来回答与视频相关的各种问题，同时将所需的 GFLOPs 从 150-360 减少到只有 67，实现了高效的视频问答模型，并在 MSRVTT-QA，MSVD-QA，IVQA 等多个数据集上实验评估，优于以往的最先进技术。