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MIRA-CAP:用于最先进的图像和视频标题的内存集成检索增强标题
Sabina Umirzakova1, Shakhnoza Muksimova1, Sevara Mardieva1
1Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
MIRA-CAP是一个新的框架,通过整合内存,修剪噪音数据,并使用流媒体解码器进行实时生成来增强图像和视频标题. 这提高了视觉语言模型的时间一致性和准确性.
科学领域:
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 准确的图像和视频标题对于辅助技术和内容推等应用至关重要.
- 挑战包括保持时间连贯性,处理杂的数据集,实现实时字幕生成.
研究的目的:
- 介绍MIRA-CAP (Memory-Integrated Retrieval-Augmented Captioning),这是一个新的框架,旨在解决目前视觉标题的局限性.
- 提高时间一致性,数据对齐,并为静态和动态内容实现实时标题.
主要方法:
- 实现了一个跨模态内存库,用于从以前的中检索相关的上下文.
- 使用自适应数据集修剪机制来过噪音数据并提高概括性.
- 开发了一种流媒体解码器,用于在没有完整序列访问的情况下逐步实时生成字幕.
主要成果:
- 在基准数据集 (MS COCO,YouCook2,ActivityNet,Flickr30k) 上,MIRA-CAP取得了最先进的结果.
- 在CIDEr,SPICE和Polos等指标上表现出很高的表现,这表明它与人类判断力有很强的一致性.
- 在处理复杂的视觉和时间结构方面展示了有效性.
结论:
- MIRA-CAP为静态和动态视觉标题任务提供了强大的和可扩展的解决方案.
- 该框架提升了视觉语言模型在现实世界应用中的功能.
- 记忆集成,数据修剪和流式解码方面的创新解决了自动化标题生成的关键挑战.
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