通过结构的时空空间对齐来增强视频语言表示
概括
本研究介绍了Finsta,一种新的方法,通过对准细粒度的结构时空信息来增强视频语言模型 (VLM). 芬斯塔可以提高各种任务的VLM性能,而不需要重新训练或场景图标注.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 大规模的视频语言模型 (VLM) 是有前途的,但在粗的对齐,时间动态和独立的模式方面存在困难.
- 现有的方法往往缺乏对视频和文本内的空间和时间关系的细粒度理解.
研究的目的:
- 通过引入一个名为 Finsta. 的细粒度结构时空对齐学习方法来增强视频语言模型 (VLM).
- 改善现有VLM的跨模式对齐,时间建模和整体性能.
主要方法:
- 使用细粒度场景图 (SG) 结构表示文本和视频,统一为整体的SG (HSG).
- 采用图形变压器进行文本式SG (TSG) 编码,并使用循环图形变压器进行视频动态SG (DSG) 和HSG建模.
- 使用时空高斯微分图转换器来捕捉跨维的对象变化,并执行以对象为中心的空间和以预言为中心的时间对齐.
主要成果:
- Finsta 持续改进了现有的 13 个高性能 VLM,跨越 6 个视频语言任务和 12 个数据集.
- 在微调和零射击设置中都实现了显著的最先进的性能增强.
- 插即用系统无集成,不需要从头开始的训练或下游的场景图表注释.
结论:
- Finsta提供了一种有效的插件解决方案,用于增强VLM,以增强时空理解.
- 该方法显著提高了各种视频语言任务的性能,建立了新的最先进的基准.
- 芬斯塔在不需要广泛的再培训的情况下改进现有模型的能力使其成为VLM研究和应用的多功能工具.
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