LVOS:用于大规模长期视频对象分割的基准
IEEE transactions on pattern analysis and machine intelligence
|September 17, 2025
概括
现有的视频对象细分 (VOS) 基准缺乏长期挑战. 新的LVOS基准显示,由于长视频和现实世界的复杂性,VOS模型的性能大幅下降.
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:
- 视频对象细分 (VOS) 模型在短期基准上表现出色.
- 当前的基准并不代表现实世界的VOS挑战,例如长期对象的可见性和重新出现.
- 需要数据集来反映实际的VOS场景.
研究的目的:
- 引入一个新的基准,LVOS,用于在现实的长期场景中评估VOS模型.
- 评估现有的VOS模型在具有挑战性的现实世界视频数据上的性能.
- 在实际应用中确定阻碍VOS性能的关键因素.
主要方法:
- 开发了长期视频对象分割 (LVOS) 基准,720个视频,296,401个和407,945个注释.
- 包括各种各样的属性,反映现实世界的挑战:长期的重现,跨时代的相似性,和封闭.
- 在三个设置下对LVOS评估了15个最先进的VOS模型.
主要成果:
- 据LVOS基准指标显示,现有VOS模型的性能大幅下降.
- 视频长度的增加,长时间的重现,跨时间的混乱和闭塞被认为是主要的挑战.
- 模型在长时间,复杂的视频中难以准确跟踪和细分.
结论:
- LVOS基准有效地突出了当前VOS模型在现实应用中的局限性.
- 长视频持续时间和复杂的相互作用显著降低VOS性能.
- 对于推进用于现实场景的VOS开发,LVOS至关重要.
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