关系增量场景图形生成由一个分割和征服管道与功能适配器
概括
本研究介绍了关系增量场景图形生成 (RISGG) 以逐步学习图像中的对象关系. 拟议的DaCFA-Net通过解决增量学习中的旧类和背景转变,显著提高了绩效.
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:
- 场景图形生成 (SGG) 识别图像中对象之间的语义关系.
- 增量学习带来了诸如旧阶级转移和SGG的背景转移等挑战.
研究的目的:
- 解决关系增量场景图形生成 (RISGG) 的挑战.
- 为有效的RISGG提出一个新的网络,DaCFA-Net.
主要方法:
- 划分和征服 (DaC) 管道将关系类识别解,以减轻旧的类转移.
- 功能适配器 (FA) 桥梁具有空间差距,并挖掘旧的关系信息.
- 结合在一起的DAC和FA构成了DACFA-Net架构.
主要成果:
- 在基准数据集上,DaCFA-Net表现出了显著的绩效增长.
- 拟议的方法在VG数据集上的现有SGG基线上实现了大约20%的改进.
结论:
- 在RISGG中,DaCFA-Net有效地处理了旧的阶级和背景转移.
- 该方法提供了一个强大的解决方案,用于在场景图表生成中的增量学习.
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