Knowledge Commons of Institute of Automation,CAS
Learning to Learn Cropping Models for Different Aspect Ratio Requirements | |
Li, Debang1,2![]() ![]() ![]() | |
2020-06 | |
会议名称 | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 |
会议录名称 | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 |
页码 | 12685-12694 |
会议日期 | 14-19, June, 2020 |
会议地点 | Virtual |
会议录编者/会议主办者 | IEEE ; CVF |
出版者 | IEEE |
摘要 | Image cropping aims at improving the framing of an image by removing its extraneous outer areas, which is widely used in the photography and printing industry. In some cases, the aspect ratio of cropping results is specified depending on some conditions. In this paper, we propose a meta-learning (learning to learn) based aspect ratio specified image cropping method called Mars, which can generate cropping results of different expected aspect ratios. In the proposed method, a base model and two meta-learners are obtained during the training stage. Given an aspect ratio in the test stage, a new model with new parameters can be generated from the base model. Specifically, the two meta-learners predict the parameters of the base model based on the given aspect ratio. The learning process of the proposed method is learning how to learn cropping models for different aspect ratio requirements, which is a typical meta-learning process. In the experiments, the proposed method is evaluated on three datasets and outperforms most state-of-the-art methods in terms of accuracy and speed. In addition, both the intermediate and final results show that the proposed model can predict different cropping windows for an image depending on different aspect ratio requirements. |
URL | 查看原文 |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China[61876181] ; National Natural Science Foundation of China[61673375] ; National Natural Science Foundation of China[61721004] ; Chinese Academy of Science[QYZDB-SSW-JSC006] |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44365 |
专题 | 复杂系统认知与决策实验室_智能系统与工程 |
通讯作者 | Huang, Kaiqi |
作者单位 | 1.CRISE, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 3.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li, Debang,Zhang, Junge,Huang, Kaiqi. Learning to Learn Cropping Models for Different Aspect Ratio Requirements[C]//IEEE, CVF:IEEE,2020:12685-12694. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
09156674.pdf(1065KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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