计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (10): 1-6.doi: 10.11896/jsjkx.230600035

• 粒计算与知识发现 • 上一篇    下一篇

基于序贯三支决策的半监督目标检测算法

宋法兴, 苗夺谦, 张红云   

  1. 同济大学电子与信息工程学院 上海201804
    嵌入式系统与服务计算教育部重点实验室 上海201804
  • 收稿日期:2023-06-05 修回日期:2023-08-08 出版日期:2023-10-10 发布日期:2023-10-10
  • 通讯作者: 苗夺谦(dqmiao@tongji.edu.cn)
  • 作者简介:(2130767@tongji.edu.cn)
  • 基金资助:
    国家重点研发计划(2022YFB3104700);国家自然科学基金(61976158,61976160,62076182,62163016,62006172);江西省自然科学基金重点项目(20212ACB202001);江西省“双千计划”

Semi-supervised Object Detection with Sequential Three-way Decision

SONG Faxing, MIAO Duoqian, ZHANG Hongyun   

  1. College of Electronic and Information Engineering,Tongji University, Shanghai 201804,China
    Key Laboratory of Embedded System and Service Computing,Ministry of Education,Shanghai 201804,China
  • Received:2023-06-05 Revised:2023-08-08 Online:2023-10-10 Published:2023-10-10
  • About author:SONG Faxing,born in 1999,postgra-duate.His main research interests include deep learning,object detection and semi-supervised object detection.MIAO Duoqian,born in 1964,Ph.D,professor,Ph.D supervisor.His main research interests include artificial intelligence,machine learning,rough set and big data analysis.
  • Supported by:
    National Key Research and Development Program of China(2022YFB3104700),National Natural Science Foundation of China(61976158,61976160,62076182,62163016,62006172),Key Project of Natural Science Fundation of Jiangxi Province,China(20212ACB202001) and Jiangxi “Double Thousand Plan”.

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摘要： 深度学习对大规模数据的需求以及目标检测标注任务的复杂性促进了半监督目标检测任务的发展。近年来,半监督目标检测已经取得了很多优秀的成果。然而,伪标签中的不确定性依然是半监督目标检测研究中难以避免的问题,优越的半监督方法要求选取合适的过滤阈值来权衡伪标签的噪声信息比例和召回率,以最大程度保留准确有效的伪标签。为了解决此问题,在半监督检测的框架中引入了序贯三支决策算法,将模型输出的伪标签根据不同的筛选阈值划分为干净的前景标签、有噪声的前景标签,以及干净的背景标签,并对其采取不同的处理策略。对有噪声的前景标签采用负类学习损失来学习这些存在噪声的标签,避免学习到其中的噪声信息。实验结果表明了所提算法的性能优势,针对COCO数据集,在有监督数据占比只有10%的情况下,该方法实现了35.2%的检测精度,相比仅依靠有监督训练性能提升了11.34%。

关键词: 序贯三支决策, 不确定性, 负类学习, 半监督学习, 半监督目标检测

Abstract: The need for large scale data in deep learning and the complexity of object detection annotation task promote the deve-lopment of semi-supervised object detection.In recent years,semi-supervised object detection has achieved many excellent results.However,the uncertainty in pseudo labels is still an unavoidable problem in semi-supervised object detection.The superior semi-supervised method requires an appropriate filtering threshold to balance the proportion of pseudo labels' noise and the recall rate,so as to retain accurate and effective labels as much as possible.To solve this problem,this paper introduces a sequential three-way decision algorithm into semi-supervised object detection,which divides the model output pseudo-labels into clean foreground labels,noisy foreground labels,and clean background labels according to different filtering thresholds,and adopts different processing strategies for them.For noisy foreground labels,we use negative class learning loss to learn these noisy labels,thereby avoiding learning noise information from them.Experimental results show the performance advantage of this algorithm.For COCO dataset,this method achieves performance of 35.2% when supervised data only accounts for 10%,which outperforms the supervised results by 11.34%.

Key words: Sequential three-way decisions, Uncertaint, Negative class learning, Semi-supervised learning, Semi-supervised object detection

中图分类号: 

  • TP389.1
[1]XU Y,SHANG L,YE J,et al.Dash:Semi-supervised learningwith dynamic thresholding[C]//International Conference on Machine Learning(ICML).Cambridge MA:JMLR,2021:11525-11536.
[2]YUE X D,CHEN Y F,MIAO D Q,et al.Fuzzy NeighborhoodCovering for Three-way Classification[J].Information Sciences,2020,507:795-808.
[3]WEI X S,XU H Y,ZHANG F,et al.An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning[J].Advances in Neural Information Processing Systems,2022,35:14489-14500.
[4]LIN T Y,MAIRE M,BELONGIE S,et al.Microsoft COCO:Common Objects in Context[C]//European Conference on Computer Vision(ECCV).Cham:Springer,2014:740-755.
[5]LIU W,ANGUELOV D,ERHAN D,et al.Ssd:Single shotmultibox detector[C]//European Conference on Computer Vision(ECCV).Cham:Springer,2016:21-37.
[6]LIN T Y,GOYAL P,GIRSHICK R,et al.Focal Loss for Dense Object Detection[C]// Conference on Computer Vision(ICCV).Cham:Springer,2017:2980-2988.
[7]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Computer Vision and Pattern Recognition(CVPR).NJ:IEEE,2014:1714-1722.
[8]GIRSHICK R.Fast R-CNN[C]// International Conference on Computer Vision(ICCV).Cham:Springer,2015:1440-1448.
[9]REN S,HE K M,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2017,39(6):1137-1149.
[10]DUAN K W,BAI S,XIE L X,et al.CenterNet:Keypoint Triplets for Object Detection[C]// IEEE/CVF International Confe-rence on Computer Vision(ICCV).Cham:Springer,2019:1-16.
[11]TIAN Z,SHEN C H,CHEN H,et al.FCOS:Fully Convolu-tional One-Stage Object Detection[C]// International Confe-rence on Computer Vision(ICCV).NJ:IEEE,2019:9627-9636.
[12]PHAM H,DAI Z,XIE Q,et al.Meta pseudo labels[C]//Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition(CVPR).NJ:IEEE,2021:11557-11568.
[13]XIE Q,LUONG M T,HOVY E,et al.Self-training with noisy student improves imagenet classification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).NJ:IEEE,2020:10687-10698.
[14]ZOPH B,GHIASI G,LIN T Y,et al.Rethinking pre-trainingand self-training[J].Advances in Neural Information Processing Systems,2020,33:3833-3845.
[15]DEVRIES T,TAYLOR G W.Improved regularization of convolutional neural networks with cutout[J].arXiv:1708.04552,2017.
[16]ZHANG H,CISSE M,DAUOHIN Y N,et al.mixup:Beyond empirical risk minimization[J].arXiv:1710.09412,2017.
[17]YUN S,HAN D,OH S J,et al.Cutmix:Regularization strategy to train strong classifiers with localizable features[C]//Procee-dings of the IEEE/CVF International Conference on Computer Vision(ICCV).NJ:IEEE,2019:6023-6032.
[18]SOHN K,ZHANG Z,LI C L,et al.A simple semi-supervisedlearning framework for object detection[J].arXiv:2005.04757,2020.
[19]LI H,WU Z,SHRIVASTAVA A,et al.Rethinking pseudo labels for semi-supervised object detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence(AAAI).CA:AAAI,2022:1314-1322.
[20]KIM J M,JANG J Y,SEO S,et al.MUM:Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).NJ:IEEE,2022:14492-14501.
[21]CHEN B,LI P,CHEN X,et al.Dense learning based semi-supervised object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).NJ:IEEE,2022:4815-4824.
[22]CHEN C,DEBATTISTA K,HAN J.Semi-supervised object detection via virtual category learning[J].arXiv:2207.03433,2022.
[23]XU M,ZHGANG Z,HU H,et al.End-to-end semi-supervisedobject detection with soft teacher[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision(ICCV).NJ:IEEE,2021:3060-3069.
[24]EVERINGHAM M,VAN GOOL L,WILLIAMS C K I,et al.The pascal visual object classes(voc) challenge[J].International Journal of Computer Vision,2009,88:303-308.
[25]CHEN K,WANG J,PANG J,et al.MMDetection:Open mmlab detection toolbox and benchmark[J].arXiv:1906.07155,2019.
[26]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[27]JEONG J,LEE S,KIM J,et al.Consistency-based semi-super-vised learning for object detection[C]//ICCV 2019.2019.
[28]ZHOU Q,YU C,WANG Z,et al.Instant-teaching:An end-to-end semi-supervised object detection framework[C]//Procee-dings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).NJ:IEEE,2021:4081-4090.
[29]YANG Q,WEI X,WANG B,et al.Interactive self-training with mean teachers forsemi-supervised object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).NJ:IEEE,2021:5941-5950.
[30]TANG Y,CHEN W,LUO Y,et al.Humble teachers teach better students for semi-supervised object detection[C]//Procee-dings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).NJ:IEEE,2021:3132-3141.
[31]LIU Y C,MA C Y,HE Z,et al.Unbiased teacher for semi-supervised object detection[J].arXiv:2102.09480,2021.
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