YOLO-CORE: Contour Regression for Efficient Instance Segmentation

doi:10.1007/s11633-022-1379-3

CASIA OpenIR > 学术期刊 > Machine Intelligence Research

	YOLO-CORE: Contour Regression for Efficient Instance Segmentation
	Haoliang Liu; Wei Xiong; Yu Zhang
发表期刊	Machine Intelligence Research
ISSN	2731-538X
	2023
卷号	20 期号:5 页码:716-728
摘要	Instance segmentation has drawn mounting attention due to its significant utility. However, high computational costs have been widely acknowledged in this domain, as the instance mask is generally achieved by pixel-level labeling. In this paper, we present a conceptually efficient contour regression network based on the you only look once (YOLO) architecture named YOLO-CORE for in stance segmentation. The mask of the instance is efficiently acquired by explicit and direct contour regression using our designed multi order constraint consisting of a polar distance loss and a sector loss. Our proposed YOLO-CORE yields impressive segmentation performance in terms of both accuracy and speed. It achieves 57.9% AP@0.5 with 47 FPS (frames per second) on the semantic boundaries dataset (SBD) and 51.1% AP@0.5 with 46 FPS on the COCO dataset. The superior performance achieved by our method with explicit contour regression suggests a new technique line in the YOLO-based image understanding field. Moreover, our instance segmentation design can be flexibly integrated into existing deep detectors with negligible computation cost (65.86 BFLOPs (billion float operations per second) to 66.15 BFLOPs with the YOLOv3 detector).
关键词	Computer vision, instance segmentation, object shape prediction, contour regression, polar distance
DOI	10.1007/s11633-022-1379-3
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/56006
专题	学术期刊_Machine Intelligence Research
作者单位	School of Computer Science and Engineering and the Key Laboratory of Computer Network and Information Integration (Ministry of Education), Southeast University, Nanjing 211189, China
推荐引用方式 GB/T 7714	Haoliang Liu,Wei Xiong,Yu Zhang. YOLO-CORE: Contour Regression for Efficient Instance Segmentation[J]. Machine Intelligence Research,2023,20(5):716-728.
APA	Haoliang Liu,Wei Xiong,&Yu Zhang.(2023).YOLO-CORE: Contour Regression for Efficient Instance Segmentation.Machine Intelligence Research,20(5),716-728.
MLA	Haoliang Liu,et al."YOLO-CORE: Contour Regression for Efficient Instance Segmentation".Machine Intelligence Research 20.5(2023):716-728.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
MIR-2022-04-108.pdf（11569KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载