Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting | |
Gao, Jiaqi1; Huang, Zhizhong1; Lei, Yiming1; Shan, Hongming2,3,4; Wang, James Z.5; Wang, Fei-Yue6,7,8; Zhang, Junping1 | |
刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
2023-12-13 | |
页码 | 14 |
关键词 | Crowd counting feature pyramid ranking semi-supervised learning |
ISSN号 | 2162-237X |
DOI | 10.1109/TNNLS.2023.3336774 |
通讯作者 | Wang, Fei-Yue(feiyue.wang@ia.ac.cn) ; Zhang, Junping(jpzhang@fudan.edu.cn) |
英文摘要 | Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. |
资助项目 | National Natural Science Foundation of China |
WOS关键词 | PEDESTRIAN DETECTION ; NETWORK |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:001127680600001 |
资助机构 | National Natural Science Foundation of China |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/55009] |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Wang, Fei-Yue; Zhang, Junping |
作者单位 | 1.Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China 2.Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China 3.Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai 200433, Peoples R China 4.Shanghai Ctr Brain Sci & Brain Inspired Technol, Shanghai 201210, Peoples R China 5.Penn State Univ, Coll Informat Sci & Technol, University Pk, PA 16802 USA 6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 7.Macau Univ Sci & Technol, Inst Syst Engn, Macau, Peoples R China 8.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China |
推荐引用方式 GB/T 7714 | Gao, Jiaqi,Huang, Zhizhong,Lei, Yiming,et al. Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2023:14. |
APA | Gao, Jiaqi.,Huang, Zhizhong.,Lei, Yiming.,Shan, Hongming.,Wang, James Z..,...&Zhang, Junping.(2023).Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,14. |
MLA | Gao, Jiaqi,et al."Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023):14. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
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