计算机科学 ›› 2022, Vol. 49 ›› Issue (6): 210-216.doi: 10.11896/jsjkx.210300267
朱旭东, 熊贇
ZHU Xu-dong, XIONG Yun
摘要： 与一般图像分类场景下的数据分布情况不同,在图像多标签分类问题的场景下,不同标签类别之间存在样本数量分布不均衡,少量头部类别通常占据大多数样本数量的情况。而由于多个标签间同时标记的相关性,再加上多标签下困难样本的分布还与数据分布和类别分布相关,使得单标签问题中解决数据不平衡的重采样等方法在多标签场景下无法有效适用。文中提出了一种基于图像多标签场景下样本分布损失和深度学习的分类方法。首先对多标签数据不均衡分布设置类别相关重采用损失,并通过动态学习方式防止分布过度异化,然后设计非对称样本学习损失,设置对正负样本和困难样本的不同学习能力,同时通过软化样本学习权重减少信息丢失。相关数据集的实验显示,所提算法在解决多标签数据分布不均衡场景下的样本学习问题时取得了很好的效果。
中图分类号:
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