Sparse Instance Activation for Real-Time Instance Segmentation
In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers. In contrast, we prop...
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| Vydané v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 4423 - 4432 |
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01.01.2022
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| ISSN: | 1063-6919 |
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| Abstract | In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers. In contrast, we propose a sparse set of instance activation maps, as a new object representation, to high-light informative regions for each foreground object. Then instance-level features are obtained by aggregating features according to the highlighted regions for recognition and segmentation. Moreover, based on bipartite matching, the instance activation maps can predict objects in a one-to-one style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to the simple yet effective designs with instance activation maps, SparseInst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark, which significantly out-performs the counterparts in terms of speed and accuracy. Code and models are available at https://github.com/hustvl/SparseInst. |
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| AbstractList | In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers. In contrast, we propose a sparse set of instance activation maps, as a new object representation, to high-light informative regions for each foreground object. Then instance-level features are obtained by aggregating features according to the highlighted regions for recognition and segmentation. Moreover, based on bipartite matching, the instance activation maps can predict objects in a one-to-one style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to the simple yet effective designs with instance activation maps, SparseInst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark, which significantly out-performs the counterparts in terms of speed and accuracy. Code and models are available at https://github.com/hustvl/SparseInst. |
| Author | Cheng, Tianheng Zhang, Wenqiang Wang, Xinggang Zhang, Qian Huang, Chang Chen, Shaoyu Zhang, Zhaoxiang Liu, Wenyu |
| Author_xml | – sequence: 1 givenname: Tianheng surname: Cheng fullname: Cheng, Tianheng email: thch@hust.edu.cn organization: School of EIC, Huazhong University of Science & Technology – sequence: 2 givenname: Xinggang surname: Wang fullname: Wang, Xinggang email: xgwang@hust.edu.cn organization: School of EIC, Huazhong University of Science & Technology – sequence: 3 givenname: Shaoyu surname: Chen fullname: Chen, Shaoyu email: shaoyuchen@hust.edu.cn organization: School of EIC, Huazhong University of Science & Technology – sequence: 4 givenname: Wenqiang surname: Zhang fullname: Zhang, Wenqiang email: wq_zhang@hust.edu.cn organization: School of EIC, Huazhong University of Science & Technology – sequence: 5 givenname: Qian surname: Zhang fullname: Zhang, Qian email: qian01.zhang@horizon.ai organization: Horizon Robotics – sequence: 6 givenname: Chang surname: Huang fullname: Huang, Chang email: chang.huang@horizon.ai organization: Horizon Robotics – sequence: 7 givenname: Zhaoxiang surname: Zhang fullname: Zhang, Zhaoxiang email: zhaoxiang.zhang@ia.ac.cn organization: Institute of Automation, Chinese Academy of Sciences (CASIA) – sequence: 8 givenname: Wenyu surname: Liu fullname: Liu, Wenyu email: liuwy@hust.edu.cn organization: School of EIC, Huazhong University of Science & Technology |
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| Snippet | In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance... |
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| SubjectTerms | Aggregates Benchmark testing categorization Computer vision Convolutional codes grouping and shape analysis; Recognition: detection Object detection Pattern recognition Real-time systems retrieval Segmentation |
| Title | Sparse Instance Activation for Real-Time Instance Segmentation |
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