计算机科学
人工智能
性格(数学)
模式识别(心理学)
字符识别
分类器(UML)
特征提取
光学字符识别
机器学习
人工神经网络
深度学习
图像(数学)
支持向量机
作者
Yonggang Li,Yafeng Zhou,Yongtao Wang,Xiaoran Qin,Zhi Tang
出处
期刊:International Conference on Pattern Recognition
日期:2021-01-10
卷期号:: 2166-2171
被引量:1
标识
DOI:10.1109/icpr48806.2021.9412282
摘要
Manga character recognition is a key technology for manga character retrieval and verification. This task is very challenging since the manga character images have a long-tailed distribution and large quality variations. Training models with cross-entropy softmax loss on such imbalanced data would introduce biases to feature and class weight norms. To handle this problem, we propose a novel dual loss which is the sum of two losses: dual ring loss and dual adaptive re-weighting loss. Dual ring loss combines weight and feature soft normalization and serves as a regularization term to softmax loss. Dual adaptive re-weighting loss re-weights softmax loss according to the norm of both feature and class weight. With the proposed losses, we have achieved encouraging results on the Manga109 benchmark. Specifically, compared with the baseline softmax loss, our method improves the character retrieval mAP from 35.72% to 38.88% and the character verification accuracy from 87.00% to 88.50%.
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