计算机科学 ›› 2023, Vol. 50 ›› Issue (10): 119-125.doi: 10.11896/jsjkx.220900196
郑世杰, 王高才
ZHENG Shijie, WANG Gaocai
摘要： 针对细粒度图像分类中高类内差异和低类间差异的挑战,提出一种以ConvNeXt网络为主干,使用GradCAM热图进行裁剪和注意力擦除的多分支细粒度图像分类方法。该方法利用GradCAM通过梯度回流得到网络的注意力热图,定位到具有判别性特征的区域,裁剪并放大该区域,使网络关注局部更深层次的特征。同时引入有监督的对比学习,扩大类间差异,减小类内差异。最后进行热图注意力擦除操作,使网络在关注最具判别性特征的前提下,也能关注其他对分类有用的区域。所提方法在CUB-200-2011,Stanford Cars,FGVC Aircraft和Stanford Dogs数据集上的分类准确率分别达到了91.8%,94.9%,94.0%,94.4%,优于多种主流的细粒度图像分类方法,并且在CUB-200-2011和Stanford Dogs数据集上分别达到了top-3和top-1的分类准确率。
中图分类号:
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