可靠的事件生成与可逆的条件规范化流量
IEEE transactions on pattern analysis and machine intelligence
|October 23, 2023
概括
本研究引入了一种用于生成事件流的新方法,从而改善视觉场景理解. 该方法学习双向映射,以获得更可靠的事件流生成和更好的摄像机性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 事件流通过强度变化和噪音捕捉视觉场景动态.
- 目前的事件生成方法使用有限的单向映射,阻碍了对各种场景和事件摄像头的适应性.
研究的目的:
- 开发一种新的方法来学习事件流特征及其参数之间的双向映射.
- 为各种应用增强事件流生成的概括能力.
主要方法:
- 通过使用具有随机生成参数的事件模拟器合成大规模事件流.
- 提出了一个基于事件的规范化流网络,以学习事件流表示和参数之间的可逆映射.
- 结合了强度引导的条件同源模拟机制和事件先验的新事件损失.
主要成果:
- 拟议的框架在视频重建,光流估计和参数估计方面取得了卓越的性能.
- 在各种合成和现实世界的数据集,场景和摄像头中展示了出色的概括性.
- 在关键计算机视觉任务中超越了最先进的方法.
结论:
- 双向映射方法显著提高了事件流生成可靠性和概括性.
- 该方法通过学习的映射和专门的损失函数有效地集成事件先验.
- 这项工作通过提供更强大,更适应性的事件流生成框架来推进基于事件的视觉.
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