Skip to main content
SpringerLink
Log in

A survey of semi- and weakly supervised semantic segmentation of images

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Image semantic segmentation is one of the most important tasks in the field of computer vision, and it has made great progress in many applications. Many fully supervised deep learning models are designed to implement complex semantic segmentation tasks and the experimental results are remarkable. However, the acquisition of pixel-level labels in fully supervised learning is time consuming and laborious, semi-supervised and weakly supervised learning is gradually replacing fully supervised learning, thus achieving good results at a lower cost. Based on the commonly used models such as convolutional neural networks, fully convolutional networks, generative adversarial networks, this paper focuses on the core methods and reviews the semi- and weakly supervised semantic segmentation models in recent years. In the following chapters, existing evaluations and data sets are summarized in details and the experimental results are analyzed according to the data set. The last part of the paper is an objective summary. In addition, it points out the possible direction of research and inspiring suggestions for future work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Arbeláez PA, Pont-Tuset J, Barron JT, Marqués F, Malik J (2014) Multiscale combinatorial grouping. In: 2014 IEEE conference on computer vision and pattern recognition, pp 328–335

  • Bo Z, Feng J, Xiao W, Yan S (2017) A survey on deep learning-based fine-grained object classification and semantic segmentation. Int J Autom Comput 14(2):119–135

    Google Scholar 

  • Chaudhry A, Dokania PK, Torr PHS (2017) Discovering class-specific pixels for weakly-supervised semantic segmentation. arXiv:1707.05821

  • Chen X, Gupta A (2015) Webly supervised learning of convolutional networks. In: 2015 IEEE international conference on computer vision (ICCV), pp 1431–1439

  • Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. Comput Sci 4:357–361

    Google Scholar 

  • Chen T, Li M, Li Y, Lin M, Wang N, Wang M, Xiao T, Xu B, Zhang C, Zhang Z (2015) Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems. arXiv:1512.01274

  • Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2016) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40:834–848

    Google Scholar 

  • Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv abs/1706.05587

  • Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder–decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801–818

  • Chen K, Pang J, Wang J, Xiong Y, Li X, Sun S, Feng W, Liu Z, Shi J, Ouyang W, et al. (2019) Hybrid task cascade for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4974–4983

  • Cholakkal H, Sun G, Khan FS, Shao L (2019) Object counting and instance segmentation with image-level supervision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 12397–12405

  • Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3213–3223

  • Dai J, He K, Sun J (2015) Boxsup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: 2015 IEEE international conference on computer vision (ICCV), pp 1635–1643

  • Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2012) The Pascal visual object classes challenge 2012 (VOC2012) results. http://www.pascal-network.org/challenges/VOC/voc2011/workshop/index.html.2011. Accessed 4 Dec 2019

  • Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59:167–181

    Google Scholar 

  • Fischer V, Kumar MC, Metzen JH, Brox T (2017) Adversarial examples for semantic image segmentation. arXiv:1703.01101

  • Gao C, Yao R, Zhao J, Zhou Y, Hu F, Li L (2019) Structure-aware person search with self-attention and online instance aggregation matching. Neurocomputing 369:29–38

    Google Scholar 

  • Garcia-Garcia A, Orts S, Oprea S, Villena-Martinez V, Rodríguez JG (2017) A review on deep learning techniques applied to semantic segmentation. arXiv:1704.06857

  • Garcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Martinez-Gonzalez P, Garcia-Rodriguez J (2018) A survey on deep learning techniques for image and video semantic segmentation. Appl Soft Comput 70:41–65

    Google Scholar 

  • Ge W, Yang S, Yu Y (2018) Multi-evidence filtering and fusion for multi-label classification, object detection and semantic segmentation based on weakly supervised learning. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 1277–1286

  • Geng Q, Zhou Z, Cao X (2016) Survey of recent progress in semantic image segmentation with cnns. Sci China Inf Sci 61:1–18

    MathSciNet  Google Scholar 

  • Girshick RB (2015) Fast r-cnn. In: 2015 IEEE international conference on computer vision (ICCV), pp 1440–1448

  • Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

  • Goceri E (2015) Effects of chosen scalar products on gradient descent algorithms. In: The 28th international conference of the Jangjeon mathematical society (ICJMS), Antalya, Turkey, p 115

  • Goceri E (2016) Fully automated liver segmentation using sobolev gradient-based level set evolution. Int J Numer Methods Biomed Eng 32:e02765

    Google Scholar 

  • Goceri E (2017) Future healthcare: will digital data lead to better care? In: 4th world conference on health sciences (HSCI-2017), Antalya, Turkey

  • Goceri E (2018) Formulas behind deep learning success. In: International conference on applied analysis and mathematical modeling (ICAAMM), Istanbul, Turkey, p 156

  • Goceri E (2019) Diagnosis of alzheimer’s disease with sobolev gradient based optimization and 3d convolutional neural network. Int J Numer Methods Biomed Eng 35(7):e3225

    MathSciNet  Google Scholar 

  • Goceri E, Esther DM (2014) A level set method with sobolev gradient and haralick edge detection. In: The 4th world conference on information technology (WCIT 2013), November 26–27, 2013, Brussels, Belgium, vol 5, pp 131–140

  • Goceri E, Goceri N (2017) Deep learning in medical image analysis: recent advances and future trends. In: 11th international conferences on computer graphics, visualization, computer vision and image processing (CGVCVIP), pp 305–311

  • Goceri E, Gooya A (2018) On the importance of batch size for deep learning. In: International conference on mathematics (ICOMATH), an Istanbul meeting for world mathematicians, minisymposium on approximation theory & minisymposium on math education, Istanbul, Turkey

  • Goceri E, Songul C (2018) Biomedical information technology: image based computer aided diagnosis systems. In: International conference on advanced technologies, Antalya, Turkey, p 132

  • Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y (2014) Generative adversarial nets. In: NIPS, pp 2672–2680

  • Goodfellow IJ, Bengio Y, Courville AC (2015) Deep learning. Nature 521:436–444

    MATH  Google Scholar 

  • Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28:1768–1783

    Google Scholar 

  • Guo Y, Liu Y, Georgiou T, Lew MS (2017) A review of semantic segmentation using deep neural networks. Int J Multimed Inf Retr 7:87–93

    Google Scholar 

  • Guo Y, Liu Y, Georgiou T, Lew MS (2018) A review of semantic segmentation using deep neural networks. Int J Multimed Inf Retr 7(2):87–93

    Google Scholar 

  • Hamaguchi R, Fujita A, Nemoto K, Imaizumi T, Hikosaka S (2017) Effective use of dilated convolutions for segmenting small object instances in remote sensing imagery. In: 2018 IEEE winter conference on applications of computer vision (WACV), pp 1442–1450

  • He K, Gkioxari G, Dollár P, Girshick RB (2018) Mask r-cnn. IEEE Int Conf Comput Vision 2017:2961–2969

    Google Scholar 

  • Hoffman J, Wang D, Yu F, Darrell T (2016) Fcns in the wild: pixel-level adversarial and constraint-based adaptation. arXiv:161202649

  • Hong S, Noh H, Han B (2015) Decoupled deep neural network for semi-supervised semantic segmentation. In: Advances in neural information processing systems, pp 1495–1503

  • Hong S, Yeo D, Kwak S, Lee H, Han B (2017) Weakly supervised semantic segmentation using web-crawled videos. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 2224–2232

  • Hu R, Dollár P, He K, Darrell T, Girshick RB (2017) Learning to segment every thing. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 4233–4241

  • Huang Z, Wang X, Wang J, Liu W, Wang J (2018) Weakly-supervised semantic segmentation network with deep seeded region growing. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 7014–7023

  • Hung WC, Tsai YH, Liou YT, Lin YY, Yang MH (2018) Adversarial learning for semi-supervised semantic segmentation. In: BMVC

  • Jia Z, Huang X, Chang EIC, Xu Y (2017) Constrained deep weak supervision for histopathology image segmentation. IEEE Trans Med Imaging 36:2376–2388

    Google Scholar 

  • Jian M, Jung C (2016) Interactive image segmentation using adaptive constraint propagation. IEEE Trans Image Process 25(3):1301–1311

    MathSciNet  MATH  Google Scholar 

  • Jin B, Segovia MVO, Süsstrunk S (2017) Webly supervised semantic segmentation. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 1705–1714

  • Kampffmeyer M, Salberg AB, Jenssen R (2016a) Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1–9

  • Kampffmeyer M, Salberg AB, Jenssen R (2016b) Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. In: 2016 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 680–688

  • Khoreva A, Benenson R, Hosang JH, Hein M, Schiele B (2016) Simple does it: weakly supervised instance and semantic segmentation. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 1665–1674

  • Kim D, Cho D, Yoo D, Kweon IS (2017) Two-phase learning for weakly supervised object localization. In: 2017 IEEE international conference on computer vision (ICCV), pp 3554–3563

  • Kirillov A, He K, Girshick R, Rother C, Dollár P (2019) Panoptic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9404–9413

  • Kolesnikov A, Lampert CH (2016) Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In: European conference on computer vision. Springer, pp 695–711

  • Kwak S, Hong S, Han B (2017) Weakly supervised semantic segmentation using superpixel pooling network. In: AAAI

  • Lateef F, Ruichek Y (2019) Survey on semantic segmentation using deep learning techniques. Neurocomputing 338:321–348

    Google Scholar 

  • Lee J, Kim E, Lee S, Lee J, Yoon S (2019) Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5267–5276

  • Levin A, Lischinski D, Weiss Y (2006) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30:228–242

    Google Scholar 

  • Li K, Wu Z, Peng KC, Ernst J, Fu Y (2018a) Tell me where to look: guided attention inference network. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 9215–9223

  • Li Q, Arnab A, Torr PHS (2018b) Weakly- and semi-supervised panoptic segmentation. In: ECCV, pp 102–118

  • Li B, Chenli C, Xu X, Jung T, Shi Y (2019a) Exploiting computation power of blockchain for biomedical image segmentation. In: The IEEE conference on computer vision and pattern recognition (CVPR) workshops

  • Li Y, Yuan L, Vasconcelos N (2019b) Bidirectional learning for domain adaptation of semantic segmentation. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp 6936–6945

  • Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: common objects in context. In: European conference on computer vision. Springer, pp 740–755

  • Lin D, Dai J, Jia J, He K, Sun J (2016) Scribblesup: Scribble-supervised convolutional networks for semantic segmentation. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 3159–3167

  • Liu J, Sun J, Yeung Shum H (2009) Paint selection. In: SIGGRAPH 2009:69

    Google Scholar 

  • Liu X, Deng Z, Yang Y (2018) Recent progress in semantic image segmentation. Artif Intell Rev 52(2):1089–1106

    Google Scholar 

  • Liu X, Zhou Y, Zhao J, Yao R, Liu B, Zheng Y (2019) Siamese convolutional neural networks for remote sensing scene classification. IEEE Geosci Remote Sens Lett 16:1–5

    Google Scholar 

  • Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  • Lu Z, Fu Z, Xiang T, Han P, Wang L, Gao X (2017) Learning from weak and noisy labels for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(3):486–500

    Google Scholar 

  • Luc P, Couprie C, Chintala S, Verbeek J (2016) Semantic segmentation using adversarial networks. arXiv:1611.08408

  • Ma T, Latecki LJ (2013) Graph transduction learning with connectivity constraints with application to multiple foreground cosegmentation. In: 2013 IEEE conference on computer vision and pattern recognition, pp 1955–1962

  • Meng F, Li H, Ngan KN, Zeng L, Wu Q (2013) Feature adaptive co-segmentation by complexity awareness. IEEE Trans Image Process 22:4809–4824

    MathSciNet  MATH  Google Scholar 

  • Meng F, Li H, Wu Q, Luo B, Ngan KN (2017) Weakly supervised part proposal segmentation from multiple images. IEEE Trans Image Process 26:4019–4031

    MathSciNet  MATH  Google Scholar 

  • Mirakhorli J, Amindavar H (2017) Semi-supervised hierarchical semantic object parsing. In: 2017 3rd Iranian conference on intelligent systems and signal processing (ICSPIS), pp 48–53

  • Mou L, Hua Y, Zhu XX (2019) A relation-augmented fully convolutional network for semantic segmentation in aerial scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 12416–12425

  • Murez Z, Kolouri S, Kriegman DJ, Ramamoorthi R, Kim K (2017) Image to image translation for domain adaptation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 4500–4509

  • Ng AY, Jordan MI, Weiss Y (2001) On spectral clustering: analysis and an algorithm. In: NIPS, pp 849–856

  • Oh SJ, Benenson R, Khoreva A, Akata Z, Fritz M, Schiele B (2017) Exploiting saliency for object segmentation from image level labels. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 5038–5047

  • Oquab M, Bottou L, Laptev I, Sivic J (2015) Is object localization for free? Weakly-supervised learning with convolutional neural networks. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 685–694

  • Orsic M, Kreso I, Bevandic P, Segvic S (2019) In defense of pre-trained imagenet architectures for real-time semantic segmentation of road-driving images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 12607–12616

  • Papandreou G, Chen LC, Murphy K, Yuille AL (2015) Weakly- and semi-supervised learning of a dcnn for semantic image segmentation. In: Proceedings of the IEEE international conference on computer vision 1742-1750

  • Pathak D, Krähenbühl P, Darrell T (2015) Constrained convolutional neural networks for weakly supervised segmentation. In: 2015 IEEE international conference on computer vision (ICCV), pp 1796–1804

  • Pinheiro PHO, Collobert R (2014) From image-level to pixel-level labeling with convolutional networks. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1713–1721

  • Pont-Tuset J, Van Gool L (2015) Boosting object proposals: from pascal to coco. In: Proceedings of the IEEE international conference on computer vision, pp 1546–1554

  • Pont-Tuset J, Arbelaez P, Barron JT, Marques F, Malik J (2016) Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE Trans Pattern Anal Mach Intell 39(1):128–140

    Google Scholar 

  • Qi X, Liu Z, Shi J, Zhao H, Jia J (2016) Augmented feedback in semantic segmentation under image level supervision. In: ECCV, pp 90–105

  • Rajchl M, Lee MCH, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W, Kainz B, Rueckert D (2016) Deepcut: object segmentation from bounding box annotations using convolutional neural networks. IEEE Trans Med Imaging 36:674–683

    Google Scholar 

  • Redondo-Cabrera C, Baptista-Ríos M, López-Sastre RJ (2018) Learning to exploit the prior network knowledge for weakly supervised semantic segmentation. IEEE Transactions on Image Processing 28:3649–3661

    MathSciNet  MATH  Google Scholar 

  • Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99

  • Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496

    Google Scholar 

  • Rother C, Kolmogorov V, Blake AJ (2004) “grabcut”: interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23:309–314

    Google Scholar 

  • Roy A, Todorovic S (2017) Combining bottom-up, top-down, and smoothness cues for weakly supervised image segmentation. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 7282–7291

  • Russakovsky O, Bearman AL, Ferrari V, Fei-Fei L (2015) What’s the point: semantic segmentation with point supervision. In: ECCV, pp 549–565

  • Saito K, Watanabe K, Ushiku Y, Harada T (2018) Maximum classifier discrepancy for unsupervised domain adaptation. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp 3723–3732

  • Saleh F, Akbarian MSA, Salzmann M, Petersson L, Gould S, Alvarez JM (2016) Built-in foreground/background prior for weakly-supervised semantic segmentation. In: ECCV, pp 413–432

  • Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 815–823

  • Scudder H (1965) Probability of error of some adaptive pattern-recognition machines. IEEE Trans Inf Theory 11(3):363–371

    MathSciNet  MATH  Google Scholar 

  • Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2016) Grad-cam: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV), pp 618–626

  • Shaban M, Mahmood A, Al-Maadeed SA, Rajpoot N (2019) An information fusion framework for person localization via body pose in spectator crowds. Inf Fusion 51:178–188

    Google Scholar 

  • Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19(1):221–248

    Google Scholar 

  • Shen T, Lin G, Shen C, Reid ID (2018) Bootstrapping the performance of webly supervised semantic segmentation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 1363–1371

  • Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22:888–905

    Google Scholar 

  • Shotton J, Johnson M, Cipolla R (2008) Semantic texton forests for image categorization and segmentation. In: 2008 IEEE conference on computer vision and pattern recognition, pp 1–8

  • Siam M, Elkerdawy S, Jägersand M, Yogamani S (2017) Deep semantic segmentation for automated driving: taxonomy, roadmap and challenges. In: 2017 IEEE 20th international conference on intelligent transportation systems (ITSC), pp 1–8

  • Singh KK, Lee YJ (2017) Hide-and-seek: forcing a network to be meticulous for weakly-supervised object and action localization. In: 2017 IEEE international conference on computer vision (ICCV), pp 3544–3553

  • Souly N, Spampinato C, Shah M (2017) Semi supervised semantic segmentation using generative adversarial network. In: 2017 IEEE international conference on computer vision (ICCV), pp 5689–5697

  • Sultana M, Mahmood A, Javed S, Jung SK (2019) Unsupervised deep context prediction for background estimation and foreground segmentation. Mach Vis Appl 30(3):375–395

    Google Scholar 

  • Tang M, Djelouah A, Perazzi F, Boykov Y, Schroers C (2018) Normalized cut loss for weakly-supervised cnn segmentation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 1818–1827

  • Thoma M (2016) A survey of semantic segmentation. arXiv:1602.06541

  • Vernaza P, Chandraker MK (2017) Learning random-walk label propagation for weakly-supervised semantic segmentation. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 2953–2961

  • Vezhnevets A, Buhmann JM (2010) Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 3249–3256

  • Vezhnevets A, Ferrari V, Buhmann JM (2011) Weakly supervised semantic segmentation with a multi-image model. In: 2011 international conference on computer vision, pp 643–650

  • Vezhnevets A, Ferrari V, Buhmann JM (2012) Weakly supervised structured output learning for semantic segmentation. In: 2012 IEEE conference on computer vision and pattern recognition, pp 845–852

  • Wang H, Wang YI, Zhang Q, Xiang S, Pan C (2017) Gated convolutional neural network for semantic segmentation in high-resolution images. Remote Sensing 9:446

    Google Scholar 

  • Wang X, You S, Li X, Ma H (2018) Weakly-supervised semantic segmentation by iteratively mining common object features. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 1354–1362

  • Wei Y, Xia W, Huang J, Ni B, Dong J, Zhao Y, Yan S (2014) Cnn: single-label to multi-label. Computer Science arXiv:1406.5726

  • Wei Y, Liang X, Chen Y, Jie Z, Xiao Y, Zhao Y, Yan S (2016a) Learning to segment with image-level annotations. Pattern Recognit 59:234–244

    Google Scholar 

  • Wei Y, Liang X, Chen Y, Shen X, Cheng MM, Feng J, Zhao Y, Yan S (2016b) Stc: a simple to complex framework for weakly-supervised semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(11):2314–2320

    Google Scholar 

  • Wei Y, Feng J, Liang X, Cheng MM, Zhao Y, Yan S (2017) Object region mining with adversarial erasing: a simple classification to semantic segmentation approach. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 6488–6496

  • Wei Y, Xiao H, Shi H, Jie Z, Feng J, Huang TS (2018) Revisiting dilated convolution: a simple approach for weakly- and semi-supervised semantic segmentation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 7268–7277

  • Weston J, Ratle F, Mobahi H, Collobert R (2012) Deep learning via semi-supervised embedding. In: Neural networks: tricks of the trade, pp 639–655

  • Wu Z, Han X, Lin YL, Gokhan Uzunbas M, Goldstein T, Nam Lim S, Davis LS (2018) Dcan: dual channel-wise alignment networks for unsupervised scene adaptation. In: The European conference on computer vision (ECCV), pp 518–534

  • Xu J, Schwing AG, Urtasun R (2015) Learning to segment under various forms of weak supervision. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 3781–3790

  • Yu H, Yang Z, Tan L, Wang Y, Sun W, Sun M, Tang Y (2018) Methods and datasets on semantic segmentation: a review. Neurocomputing 304:82–103

    Google Scholar 

  • Zhan X, Liu Z, Luo P, Tang X, Loy CC (2017) Mix-and-match tuning for self-supervised semantic segmentation. In: AAAI

  • Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: A survey of unsupervised methods. computer vision and image understanding 110(2):260–280

  • Zhang L, Yang Y, Gao Y, Yu Y, Wang C, Li X (2014) A probabilistic associative model for segmenting weakly supervised images. IEEE Transactions on Image Processing 23:4150–4159

    MathSciNet  MATH  Google Scholar 

  • Zhang W, Huang H, Schmitz M, Sun X, Wang H, Mayer H (2017) Effective fusion of multi-modal remote sensing data in a fully convolutional network for semantic labeling. Remote Sens 10:52

    Google Scholar 

  • Zhang X, Wei Y, Feng J, Yang Y, Huang TS (2018a) Adversarial complementary learning for weakly supervised object localization. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 1325–1334

  • Zhang Y, Qiu Z, Yao T, Liu D, Mei T (2018b) Fully convolutional adaptation networks for semantic segmentation. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp 6810–6818

  • Zhang SH, Li R, Dong X, Rosin P, Cai Z, Han X, Yang D, Huang H, Hu SM (2019) Pose2seg: detection free human instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 889–898

  • Zhao B, Wang F, Zhang C (2008) Efficient multiclass maximum margin clustering. In: ICML, pp 1248–1255

  • Zhao B, Kwok JT, Zhang C (2009) Maximum margin clustering with multivariate loss function. In: 2009 ninth IEEE international conference on data mining, pp 637–646

  • Zhao A, Balakrishnan G, Durand F, Guttag JV, Dalca AV (2019) Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8543–8553

  • Zhou B, Khosla A, Lapedriza À, Oliva A, Torralba A (2015) Learning deep features for discriminative localization. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2921–2929

  • Zhou Y, Zhu Y, Ye Q, Qiu Q, Jiao J (2018) Weakly supervised instance segmentation using class peak response. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 3791–3800

  • Zhou Y, Liu X, Zhao J, Ma D, Yao R, Liu B (2019) Zheng Y (2019) Remote sensing scene classification based on rotation-invariant feature learning and joint decision making. EURASIP J Image Video Process 1:3

    Google Scholar 

Download references

Funding

Funding was provided by State’s Key Project of Research and Development Plan of China (Grant No. 2016YFC0600900), National Natural Science Foundation of China (Grant Nod. 61572505, 61772530, 61806206), Six Talent Peaks Project in Jiangsu Province (Grant Nos. 2015-DZXX-010, 2018-XYDXX-044), Natural Science Foundation of Jiangsu Province (Grant Nos. BK20180639, BK20171192, BK20180174), China Postdoctoral Science Foundation (Grant No. 2018M642359) and Innovative Research Group Project of the National Natural Science Foundation of China (Grant No. 61801198).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Man Zhang, Yong Zhou, Jiaqi Zhao, Bing Liu & Rui Yao

  2. Mine Digitization Engineering Research Center of Minstry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Man Zhang, Yong Zhou, Jiaqi Zhao, Bing Liu & Rui Yao

  3. Qian Xuesen Laboratory of Space Technology, Beijing, 100094, China

    Yiyun Man

Authors
  1. Man Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiaqi Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yiyun Man
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rui Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yong Zhou.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, M., Zhou, Y., Zhao, J. et al. A survey of semi- and weakly supervised semantic segmentation of images. Artif Intell Rev 53, 4259–4288 (2020). https://doi.org/10.1007/s10462-019-09792-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-019-09792-7

Keywords

Search

Navigation