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Object Affinity Learning: Towards Annotation-Free Instance Segmentation | |
Wang, Yuqi1,2; Chen, Yuntao3; Zhang, Zhaoxiang1,2,3 | |
发表期刊 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE |
ISSN | 0162-8828 |
2023-11-01 | |
卷号 | 45期号:11页码:13959-13973 |
通讯作者 | Zhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn) |
摘要 | We address the problem of annotation-free instance segmentation in the wild, aiming to relieve the expensive cost of manual mask annotations. Existing approaches utilize appearance cues, such as color, edge, and texture information, to generate pseudo masks for instance segmentation. However, due to the ambiguity of defining an object by visual appearance alone, these methods fail to distinguish objects from the background under complex scenes. Beyond visual cues, objects are one-piece in space and move together over time, which indicates that geometry cues, such as spatial continuity and motion consistency, are also exploitable for this problem. To directly utilize geometry cues, we propose an affinity-based paradigm for annotation-free instance segmentation. The new paradigm is called object affinity learning, a proxy task of annotation-free instance segmentation, which aims to tell whether two pixels come from the same object by learning feature representation from geometry cues. During inference, the learned object affinity could be further converted into instance segmentation masks by some graph partition algorithms. The proposed object affinity learning achieves much better instance segmentation performance than existing pseudo-mask-based methods on the large-scale Waymo Open Dataset and KITTI dataset. |
关键词 | Videos Motion segmentation Visualization Three-dimensional displays Task analysis Object detection Geometry Object affinity learning geometric information annotation-free instance segmentation |
DOI | 10.1109/TPAMI.2023.3298351 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Major Project for New Generation of AI[2018AAA0100400] ; 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] |
项目资助者 | Major Project for New Generation of AI ; National Natural Science Foundation of China |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:001085050900064 |
出版者 | IEEE COMPUTER SOC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/54407 |
专题 | 多模态人工智能系统全国重点实验室 智能感知与计算研究中心 |
通讯作者 | Zhang, Zhaoxiang |
作者单位 | 1.Chinese Acad Sci CASIA, Inst Automat, Ctr Res Intelligent Percept & Comp CRIPAC, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci HKISI CAS, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Wang, Yuqi,Chen, Yuntao,Zhang, Zhaoxiang. Object Affinity Learning: Towards Annotation-Free Instance Segmentation[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(11):13959-13973. |
APA | Wang, Yuqi,Chen, Yuntao,&Zhang, Zhaoxiang.(2023).Object Affinity Learning: Towards Annotation-Free Instance Segmentation.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(11),13959-13973. |
MLA | Wang, Yuqi,et al."Object Affinity Learning: Towards Annotation-Free Instance Segmentation".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.11(2023):13959-13973. |
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