主观-客观情绪相关生成网络用于主观视频字幕
概括
这项研究引入了主观视频标题 (SVC),通过考虑观众反应来生成个性化的情感描述. 拟议的主观客观情感相关生成网络 (SO-ECGN) 增强了视频内容中的情感理解.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 多媒体处理处理.
背景情况:
- 情感视频标题 (EVC) 专注于从视觉线索的客观描述.
- 现有的EVC方法忽视了个性化的观众情绪反应.
- 在理解视频内容中的主观情感体验方面存在差距.
研究的目的:
- 介绍和解决主观视频标题 (SVC) 的新任务.
- 为了产生反映个性化的观众反应的情感标题.
- 弥合客观情感感知和主观情感理解之间的差距.
主要方法:
- 提出了主观客观情感相关生成网络 (SO-ECGN).
- 利用动态掩盖注意力和情绪领域转移以实现客观情绪学习.
- 开发了一个主观-客观情绪相关模块,用于适应性情绪指导.
- 实现了与情感相关的解码器,用于生成主观字幕.
主要成果:
- 在具有挑战性的数据集上,SO-ECGN表现出卓越的性能.
- 在BLEU-1上达到79.2%,在EmVidCap-L数据集上的CIDEr指标达到45.1%.
- 在SO-ECGN框架内验证单个模块的有效性.
结论:
- 拟议的SO-ECGN有效地产生主观的情感标题.
- SVC是个性化视频理解的一个有希望的方向.
- 该方法成功地整合了事实和情感信息,以增强标题.
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