【推荐理由】本文采用ChatGPT模型作为控制器，用于创建更全面的时空视频描述。实验证明该方法可以生成包含更多视频细节的字幕。
Video ChatCaptioner: Towards the Enriched Spatiotemporal Descriptions
Jun Chen, Deyao Zhu, Kilichbek Haydarov, Xiang Li, Mohamed Elhoseiny
[King Abdullah University of Science and Technology]
【论文链接】https://arxiv.org/pdf/2304.04227.pdf
【项目链接】https://github.com/Vision-CAIR/ChatCaptioner
【摘要】视频字幕旨在使用自然语言传达视频中的动态场景，从而促进我们对环境中的时空信息的理解。虽然最近取得了一些进展，但生成详细和丰富的视频描述仍然是一个重大挑战。在这项工作中，作者介绍了Video ChatCaptioner，一种创新的方法，用于创建更全面的时空视频描述。该方法采用ChatGPT模型作为控制器，专门设计用于选择适合提出视频内容驱动的问题的帧。随后，使用强大的算法回答这些视觉查询。这种问答框架有效地揭示了复杂的视频细节，并显示出增强视频内容的方法的前景。在多次对话轮之后，ChatGPT可以根据以前的对话总结丰富的视频内容。实验证明了本文的Video ChatCaptioner可以生成包含更多视频细节的字幕。
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