Coarse Mask Guided Interactive Object Segmentation
文献类型：期刊论文
| 作者 | Li, Jing3,4 ; Fan, Junsong1,5; Wang, Yuxi1,5; Yang, Yuran2; Zhang, Zhaoxiang1,6,7,8
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2023 |
| 卷号 | 32页码:5808-5822 |
| 关键词 | Segmentation interactive transformer annotation tool |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2023.3322564 |
| 通讯作者 | Zhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn) |
| 英文摘要 | Interactive object segmentation aims to produce object masks with user interactions, such as clicks, bounding boxes, and scribbles. Click point is the most popular interactive cue for its efficiency, and related deep learning methods have attracted lots of interest in recent years. Most works encode click points as gaussian maps and concatenate them with images as the model's input. However, the spatial and semantic information of gaussian maps would be noised through multiple convolution layers and won't be fully exploited by top layers for mask prediction. To pass click information to top layers exactly and efficiently, we propose a coarse mask guided model (CMG) which predicts coarse masks with a coarse module to guide the object mask prediction. Specifically, the coarse module encodes user clicks as query features and enriches their semantic information with backbone features through transformer layers, coarse masks are generated based on the enriched query feature and fed into CMG's decoder. Benefiting from the efficiency of transformer, CMG's coarse module and decoder module are lightweight and computationally efficient, making the interaction process more smooth. Experiments on several segmentation benchmarks demonstrate the effectiveness of our method, and we get new state-of-the-art results compared with previous works. |
| WOS关键词 | RANDOM-WALKS ; IMAGE ; CUT |
| 资助项目 | National Natural Science Foundation of China[61836014] ; National Natural Science Foundation of China[U21B2042] ; National Natural Science Foundation of China[62072457] ; National Natural Science Foundation of China[62006231] ; InnoHK Program |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001104979200001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China ; InnoHK Program |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55098]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Zhang, Zhaoxiang |
| 作者单位 | 1.HKISI CAS, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China 2.Tencent Maps, Beijing 100101, Peoples R China 3.Chinese Acad Sci CASIA, Inst Automat, Ctr Res Intelligent Percept & Comp CRIPAC, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100190, Peoples R China 5.Chinese Acad Sci CASIA, Inst Automat, Ctr Res Intelligent Percept & Comp CRIPAC, Beijing 100190, Peoples R China 6.Chinese Acad Sci CASIA, Inst Automat, Beijing 100190, Peoples R China 7.Univ Chinese Acad Sci UCAS, Sch Future Technol, Beijing 100049, Peoples R China 8.State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Jing,Fan, Junsong,Wang, Yuxi,et al. Coarse Mask Guided Interactive Object Segmentation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:5808-5822. |
| APA | Li, Jing,Fan, Junsong,Wang, Yuxi,Yang, Yuran,&Zhang, Zhaoxiang.(2023).Coarse Mask Guided Interactive Object Segmentation.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,5808-5822. |
| MLA | Li, Jing,et al."Coarse Mask Guided Interactive Object Segmentation".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):5808-5822. |
入库方式： OAI收割
来源：自动化研究所
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