相互引导的融合学习用于协作伪装对象细分
IEEE transactions on neural networks and learning systems
|December 5, 2025
概括
我们介绍了相互引导的融合精炼网络 (MFRNet),用于协作伪装对象细分. 这种新方法增强了图像之间的特征共享,大大改善了隐藏在复杂背景中的对象的细分.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 由于对象与背景混合,协作伪装对象分割 (CoCOS) 很困难.
- 现有的方法难以利用共享的类内特征,导致复杂场景中的表现不佳.
研究的目的:
- 开发一个新的网络,即相互引导的融合精炼网络 (MFRNet),以改进CoCOS.
- 增强班内图像之间的协作和共享信息的优化.
主要方法:
- 在MFRNet中使用特征编码,单图像和多图像分支特征增强以及相互指导.
- 图形卷积自我注意 (GCS) 和空间上下文探索 (SCE) 模块增强了多层次的功能.
- 一个相互指导融合 (MGF) 模块使用跨场景信息进行渐进的改进.
主要成果:
- MFRNet的性能明显优于现有的CoCOS方法.
- 在CoCOD8K数据集上获得了0.846的平均E-测量得分.
- 在复杂的场景中展示了伪装对象的优越细分.
结论:
- 拟议的MFRNet有效地利用共享功能,以加强协作伪装对象细分.
- MFRNet架构在CoCOS性能方面取得了显著的进步,特别是在具有挑战性的环境中.
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