Knowledge Commons of Institute of Automation,CAS
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection | |
Wang, Tong1,2; Zhu, Yousong1; Chen, Yingying1; Zhao, Chaoyang1,4; Yu, Bin1,2; Wang, Jinqiao1,2,3; Tang, Ming1 | |
2022-06 | |
会议名称 | IEEE Computer Vision and Pattern Recognition (CVPR) |
会议日期 | 2022-6-19 |
会议地点 | New Orleans, Louisiana & Online |
摘要 | Long-tail object detection suffers from poor performance on tail categories. We reveal that the real culprit lies in the extremely imbalanced distribution of the classifier’s weight norm. For conventional softmax cross-entropy loss, such imbalanced weight norm distribution yields ill-conditioned decision boundary for categories which have small weight |
学科领域 | 模式识别 |
学科门类 | 工学::计算机科学与技术(可授工学、理学学位) |
收录类别 | EI |
资助项目 | National Nature Science Foundation of China[61876086] ; National Nature Science Foundation of China[61876086] |
语种 | 英语 |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47418 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 3.Peng Cheng Laboratory, Shenzhen, China 4.Development Research Institute of Guangzhou Smart City, Guangzhou, China |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Wang, Tong,Zhu, Yousong,Chen, Yingying,et al. C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection[C],2022. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
C2AM Loss Chasing a (5757KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论