计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 105-112.doi: 10.11896/jsjkx.210100108
张文轩, 吴秦
ZHANG Wen-xuan, WU Qin
摘要： 针对细粒度图像类间差距小、类内差距大的问题,文中提出以弱监督学习的方式使用多分支注意力增强卷积网络,从而实现细粒度图像分类。文中采用Inception-V3网络提取图像的基础特征,从中获取多个局部响应区域并进行特征融合,在此基础上采用注意力机制对图像关键区域进行自约束的局部裁剪和局部擦除,避免仅提取目标单个部位的特征,促使网络更加关注目标物体不同部位的细节特征,同时也提升了目标区域的定位精度。此外,文中提出中心正则化损失函数来约束训练过程中获取的注意力区域,以进一步提升目标定位精度和扩大图像特征的类间差距。在3个公开数据集上进行了实验,结果表明,所提方法取得了比当前最优方法更好的结果。
中图分类号:
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