语义辅助对象聚类用于多模式引用视频分割
IEEE transactions on pattern analysis and machine intelligence
|September 29, 2025
概括
本研究介绍了语义辅助对象集群网络 (SOC++) 用于多模式引用视频分割. 它通过统一时间交互和交叉模式对齐来提高对象识别和细分精度,以更好地理解视频.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 引用视频细分需要识别由语言线索指定的对象.
- 现有的方法因遮蔽或动作模糊而扎于框架间关系和杂的视觉数据.
- 时间建模中的香草注意力机制可能会导致混乱的表示.
研究的目的:
- 开发一个优化的模型,用于多模式引用视频细分.
- 增强使用框架间关系和全球视频内容的利用.
- 解决以前处理杂视觉嵌入和时间变化的方法的局限性.
主要方法:
- 引入了语义辅助对象集群网络 (SOC) 和其改进版本SOC++.
- 统一的时间选择性交互和交叉模式对齐,以实现视频层次的理解.
- 采用代理辅助的多模式融合,语义集成与渐进的到视频结构,以及多模式查询对比监督.
- 通过强调信息框架和动态查询融合模块,集成了倾向性视频聚合.
主要成果:
- 在受欢迎的参考视频细分基准上,SOC++显著优于最先进的竞争对手.
- 该方法显示了增强的分段稳定性和适应性,特别是对于具有时间变化的文本表达式.
- 在所有测试的基准中实现了显著的绩效利.
结论:
- 拟议的SOC++模型有效地解决了多模式引用视频细分方面的挑战.
- 对时间连贯性和跨模式对齐的强调导致了优越的视频层次理解.
- 该方法提供了一个可靠的解决方案,以语言描述为指导的准确和稳定的对象细分.
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