相关实验视频
交互式图像到视频转移学习
Cong Wu1, Tianyang Xu2, Zhenhua Feng2
1School of Artificial Intelligence and Computer Science, Jiangnan University, 214122, China; Postdoctoral Research Station in Design, Jiangnan University, 214122, China.
概括
本研究介绍了一种高效的图像到视频传输学习框架,SDST,它增强了动作识别的静态和动态提示交互. 该方法可以在不需要大量微调的情况下改善视频理解,优于现有技术.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 将学习从图像转移到视频对于动作识别至关重要.
- 目前的方法涉及广泛的微调,导致高计算成本.
- 有效的方法往往忽视了有效的视频推理与结的图像骨干.
研究的目的:
- 为动作识别提出一个高效的图像转录视频学习框架 (SDST).
- 增强静态和动态线索以及空间和时间领域之间的相互作用.
- 为了弥合静态视觉表示和基于视频的动作识别任务之间的差距.
主要方法:
- 介绍了Motion Booster模块,通过交叉注意力来提取和合并动作描述符与静态表示.
- 建议一个轻量级的通道意识多尺度时间建模模块用于时间推理.
- 用这些新的组件来丰富冷的图像骨干.
主要成果:
- 在一些基准指标上,SDST超越了最先进的高效转移学习方法,如Something-Something V1&V2,Diving-48和Kinetics-400.
- 该框架在没有额外的复杂性的情况下超过了几种完全微调的方法.
- 已证明可转移到像CLIP这样的视觉语言模型,从而进一步提高性能.
结论:
- 拟议的SDST框架为高效的视频理解提供了通用和可扩展的解决方案.
- 它有效地解决了现有的高效转移学习方法的局限性.
- 强调了在大型视觉语言模型时代改善行动识别的潜力.
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