多颗粒度对比跨模态协作生成,用于端到端的长时间视频问题解答
概括
多颗粒度对比跨模块协作生成 (MCG) 模型通过改进跨模块推理和生成答案来增强长期视频问答 (VideoQA). 这种端到端的解决方案在多个VideoQA基准上实现了卓越的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 长期视频问题答案 (VideoQA) 是复杂的,需要对未经修剪的视频和交叉模式推理的语义理解.
- 现有的方法使用特征提取器,导致域独立表示和渐变阻断问题.
- 虽然视频语言预培训模型提供了端到端的解决方案,但它们缺乏特定领域的推理,并且在任务制定上存在差异.
研究的目的:
- 为长期视频QA引入一个端到端的解决方案,解决当前方法的局限性.
- 为了获得有区别的,概念丰富的表示,用于视觉理解.
- 重构视频QA,将其视为改善跨模式融合和答案生成的生成任务.
主要方法:
- 提出了多颗粒度对比跨模式协作生成 (MCG) 模型.
- 介绍了用于歧视性表示的剪切骨架构的联合单模建 (JUM).
- 利用多颗粒度对比学习 (MCL) 来捕获语义对应.
- 开发了一个跨模式协作生成 (CCG) 模块,以重新制定视频QA作为一个生成任务.
主要成果:
- 拟议的MCG模型在六个公开可用的VideoQA数据集上展示了卓越的性能.
- JUM和MCL组件有效地导出具有高视觉概念相关性的歧视性表示.
- CCG模块成功实现了高语义融合和生成,以合理化和回答问题.
结论:
- 在终端到终端的长期视频QA.MCG模型提供了显著的进步.
- 整合JUM,MCL和CCG有效地解决了代表性学习和任务制定方面的挑战.
- 视频QA的生成方法显示了未来研究和应用的巨大潜力.
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