Knowledge Commons of Institute of Automation,CAS
Composing Good Shots by Exploiting Mutual Relations | |
Li, Debang1,2; Zhang, Junge1,2; Huang, Kaiqi1,2,3; Yang, Ming-Hsuan4,5 | |
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 | 4213-4222 |
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 | Finding views with a good composition from an input image is a common but challenging problem. There are usually at least dozens of candidates (regions) in an image, and how to evaluate these candidates is subjective. Most existing methods only use the feature corresponding to each candidate to evaluate the quality. However, the mutual relations between the candidates from an image play an essential role in composing a good shot due to the comparative nature of this problem. Motivated by this, we propose a graph-based module with a gated feature update to model the relations between different candidates. The candidate region features are propagated on a graph that models mutual relations between different regions for mining the useful information such that the relation features and region features are adaptively fused. We design a multi-task loss to train the model, especially, a regularization term is adopted to incorporate the prior knowledge about the relations into the graph. A data augmentation method is also developed by mixing nodes from different graphs to improve the model generalization ability. Experimental results show that the proposed model performs favorably against state-of-the-art methods, and comprehensive ablation studies demonstrate the contribution of each module and graph-based inference of the proposed method.https://openaccess.thecvf.com/content_CVPR_2020/html/Li_Composing_Good_Shots_by_Exploiting_Mutual_Relations_CVPR_2020_paper.html |
URL | 查看原文 |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China[61876181] ; Chinese Academy of Science[QYZDB-SSW-JSC006] ; National Natural Science Foundation of China[61673375] ; National Natural Science Foundation of China[61721004] |
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/173211/44364 |
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.CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, China 3.University of California, Merced 4.Google Research 5.School of Artificial Intelligence, University of Chinese Academy of Sciences, 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,et al. Composing Good Shots by Exploiting Mutual Relations[C]//IEEE, CVF:IEEE,2020:4213-4222. |
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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 | ||
09157704.pdf(628KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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