Mask SSD: An Effective Single-Stage Approach to Object Instance Segmentation | |
Zhang, Hui1,5; Tian, Yonglin2,5; Wang, Kunfeng3,5; Zhang, Wensheng4; Wang, Fei-Yue5 | |
刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
2020 | |
卷号 | 29期号:1页码:2078-2093 |
关键词 | Object detection instance segmentation feedback features single-shot detector |
ISSN号 | 1057-7149 |
DOI | 10.1109/TIP.2019.2947806 |
英文摘要 | We propose Mask SSD, an efficient and effective approach to address the challenging instance segmentation task. Based on a single-shot detector, Mask SSD detects all instances in an image and marks the pixels that belong to each instance. It consists of a detection subnetwork that predicts object categories and bounding box locations, and an instance-level segmentation subnetwork that generates the foreground mask for each instance. In the detection subnetwork, multi-scale and feedback features from different layers are used to better represent objects of various sizes and provide high-level semantic information. Then, we adopt an assistant classification network to guide per-class score prediction, which consists of objectness prior and category likelihood. The instance-level segmentation subnetwork outputs pixel-wise segmentation for each detection while providing the multi-scale and feedback features from different layers as input. These two subnetworks are jointly optimized by a multi-task loss function, which renders Mask SSD direct prediction on detection and segmentation results. We conduct extensive experiments on PASCAL VOC, SBD, and MS COCO datasets to evaluate the performance of Mask SSD. Experimental results verify that as compared with state-of-the-art approaches, our proposed method has a comparable precision with less speed overhead. |
资助项目 | National Natural Science Foundation of China[U1811463] ; National Key R&D Program of China[2018YFC1704400] |
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:000505788600007 |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/29443] |
专题 | 精密感知与控制研究中心_人工智能与机器学习 |
通讯作者 | Wang, Kunfeng |
作者单位 | 1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 2.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China 3.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 5.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Hui,Tian, Yonglin,Wang, Kunfeng,et al. Mask SSD: An Effective Single-Stage Approach to Object Instance Segmentation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29(1):2078-2093. |
APA | Zhang, Hui,Tian, Yonglin,Wang, Kunfeng,Zhang, Wensheng,&Wang, Fei-Yue.(2020).Mask SSD: An Effective Single-Stage Approach to Object Instance Segmentation.IEEE TRANSACTIONS ON IMAGE PROCESSING,29(1),2078-2093. |
MLA | Zhang, Hui,et al."Mask SSD: An Effective Single-Stage Approach to Object Instance Segmentation".IEEE TRANSACTIONS ON IMAGE PROCESSING 29.1(2020):2078-2093. |
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