Citation: | ZHANG Zhe, WANG Bilin, YU Zhezhou, et al., “Dilated Convolutional Pixels Affinity Network for Weakly Supervised Semantic Segmentation,” Chinese Journal of Electronics, vol. 30, no. 6, pp. 1120-1130, 2021, doi: 10.1049/cje.2021.08.007 |
J. Long, E. Shelhamer and T. Darrell, "Fully convolutional networks for semantic segmentation", Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp.3431-3440, 2015.
|
L. C. Chen, G. Papandreou, I. Kokkinos, et al., "Semantic image segmentation with deep convolutional nets and fully connected CRFs", Proc. of International Conference on Learning Representations (ICLR), San Diego, California, USA, pp.1-14, 2015.
|
J. Dai, K. He and J. Sun, "BoxSup:Exploiting bounding boxes to supervise convolutional networks for semantic segmentation", Proc. of IEEE International Conference on Computer Vision (ICCV), Santiago, Chlie, pp.1635-1643, 2015.
|
D. Lin, J. Dai, J. Jia, et al., "ScribbleSup:Scribble-supervised convolutional networks for semantic segmentation", Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp.3159-3167, 2016.
|
A. Bearman, O. Russakovsky, V. Ferrari, et al., "What's the point:Semantic segmentation with point supervision", Proc. of European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, pp.549-565, 2016.
|
P. O. Pinheiro and R. Collobert, "From image-level to pixellevel labeling with convolutional networks", Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp.1713-1721, 2015.
|
X. Qi, Z. Liu, J. Shi, et al., "Augmented feedback in semantic segmentation under image level supervision", Proc. of European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, pp.90-105, 2016.
|
Y. Wei, X. Liang, Y. Chen, et al., "STC:A simple to complex framework for weakly-supervised semantic segmentation", IEEE Trans. Pattern Anal. Mach. Intell., Vol.39, No.11, pp.2314-2320, Nov. 2017.
|
Y. Wei, J. Feng, X. Liang, et al., "Object region mining with adversarial erasing:A simple classification to semantic segmentation approach", Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp.1568-1576, 2017.
|
J. Lee, E. Kim, S. Lee, et al., "FickleNet:Weakly and semi-supervised semantic image segmentation using stochastic inference", Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp.5267-5276, 2019.
|
Y. Wei, H. Xiao, H. Shi, et al., "Revisiting dilated convolution:A simple approach for weakly-and semi-supervised semantic segmentation", Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.7268-7277, 2018.
|
M. Cordts, M. Omran, S. Ramos, et al., "The cityscapes dataset for semantic urban scene understanding", Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp.3213-3223, 2016.
|
P. Papadopoulos, A. D. F. Clarke, F. Keller, et al., "Training object class detectors from eye tracking data", Proc. of European Conference on Computer Vision (ECCV), Zurich, Switzerland, pp.361-376, 2014.
|
B. Zhou, A. Khosla, A. Lapedriza, et al., "Learning deep features for discriminative localization", Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp.2921-2929, 2016.
|
A. Kolesnikov and C. H. Lampert, "Seed, expand and constrain:Three principles for weakly-supervised image segmentation", Proc. of European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, pp.695-711, 2016.
|
Z. Huang, X. Wang, J. Wang, et al., "Weakly-supervised semantic segmentation network with deep seeded region growing", Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.7014-7023, 2018.
|
J. Ahn and S. Kwak, "Learning pixel-level semantic affifinity with image-level supervision for weakly supervised semantic segmentation", Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.4981-4990, 2018.
|
A. Chaudhry, P. K. Dokania and P. Torr, "Discovering classspecifific pixels for weakly-supervised semantic segmentation", Proc. of British Machine Vision Conference, London, UK, pp.20.1-20.13, 2017.
|
X. Wang, S. You, X. Li, et al., "Weakly-supervised semantic segmentation by iteratively mining common object features", Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.1354-1362, 2018.
|
L. -C. Chen, G. Papandreou, I. Kokkinos, et al., "DeepLab:Semantic image segmentation with deep convolutional nets atrous convolution and fully connected CRFs", IEEE Trans. Pattern Anal. Mach. Intell., Vol.40, No.4, pp.834-848, 2018.
|
L. -C. Chen, Y. Zhu, G. Papandreou, et al., "Encoderdecoder with atrous separable convolution for semantic image segmentation", Proc. of European Conference on Computer Vision (ECCV), Munich, Germany, pp.801-818, 2018.
|
J. Fu, J. Liu, H. Tian, et al., "Dual attention network for scene segmentation", Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp.3141-3149, 2019.
|
F. Lyu, L. Li, S. S. Victor, et al., "Multi-label image classification via coarse-to-fine attention", Chinese Journal of Electronics, Vol.28, No.6, pp.1118-1126, 2019.
|
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition", Proc. of International Conference on Learning Representations, San Diego, California, USA, pp.1-14, 2015.
|
K. He, X. Zhang, S. Ren, et al., "Deep residual learning for image recognition", Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp.770-778, 2016.
|
M. Everingham, L. Van Gool, C. K. I. Williams, et al., "The Pascal visual object classes (VOC) challenge", International Journal of Computer Vision, Vol.88, No.2, pp.303-338, 2010.
|
O. Ronneberger, P. Fischer, and T. Brox, "U-Net:Convolutional networks for biomedical image segmentation", Proc. of International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Munich, Germany, pp.234-241, 2015.
|
Deyu. M and Lina. Sun, "Some new trends of deep learning research", Chinese Journal of Electronics, Vol.28, No.6, pp.1087-1091, 2019.
|
G. Papandreou, L. -C. Chen, K. P. Murphy, et al., "Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation", Proc. of IEEE International Conference on Computer Vision (ICCV), Santiago, Chlie, pp.1742-1750, 2015.
|
J. Pont-Tuset, P. Arbeláez, J. T. Barron, et al., "Multiscale combinatorial grouping for image segmentation and object proposal generation", IEEE Trans. Pattern Anal. Mach. Intell., Vol.39, No.1, pp.128-140, Jan. 2017.
|
J. Fan, Z. Zhang, T. Tan, et al., "CIAN:Cross-image affinity net for weakly supervised semantic segmentation", Proc. of AAAI Conference on Artificial Intelligence, New York, NY, USA pp.10762-10769, 2020.
|
Y. Wang, J. Zhang, M. Kan, et al., "Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation", Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, pp.12272-12281, 2020.
|
J. Deng, W. Dong, R. Socher, et al., "ImageNet:A large-scale hierarchical image database", Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA, pp.248-255, 2009.
|
S. Yang, Y. Kim, Y. Kim, et al., "Combinational class activation maps for weakly supervised object localization", Proc. of IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA, pp.2930-2938, 2020.
|
P. Krähenbühl and V. Koltun, "Efficient inference in fully connected CRFS with Gaussian edge potentials", Proc. of Conference and Workshop on Neural Information Processing Systems (NIPS), Granada, Spain, pp.109-117, 2011.
|
B. Hariharan, P. Arbelaez, L. Bourdev, et al., "Semantic contours from inverse detectors", Proc. of IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, pp.991-998, 2011.
|
N. Ketkar, "Introduction to pytorch", Deep Learning with Python, eBook, https://link.springer.com/book/10.1007/978-1-4842-2766-4#toc,pp.195-208,2017.
|
Y. Jia, E. Shelhamer, J. Donahue, et al., "Caffe:Convolutional architecture for fast feature embedding, " Proceedings of the 22nd ACM International Conference on Multimedia, pp.675-678, 2014.
|
R. Fan, Q. Hou, M. -M. Cheng, et al., "Associating inter-image salient instances for weakly supervised semantic segmentation", Proc. of European Conference on Computer Vision (ECCV), Munich, Germany, pp.367-383, 2018.
|
S. Hong, J. Oh, H. Lee, et al., "Learning transferrable knowledge for semantic segmentation with deep convolutional neural network", Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp.3204-3212, 2016.
|
S. Hong, D. Yeo, S. Kwak, et al., "Weakly supervised semantic segmentation using Web-crawled videos", Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp.7322-7330, 2017.
|
P. -T. Jiang, Q. Hou, Y. Cao, et al., "Integral object mining via online attention accumulation", Proc. of IEEE International Conference on Computer Vision (ICCV), Seoul, Korea (South), pp.2070-2079, 2019.
|
T. Zhang, G. Lin, J. Cai, et al., "Decoupled spatial neural attention for weakly supervised semantic segmentation", IEEE Transactions on Multimedia, Vol.21, No.11, pp.2930-2941, 2019.
|
W. Shimoda, and K. Yanai, "Self-supervised difference detection for weakly-supervised semantic segmentation", Proc. of IEEE International Conference on Computer Vision (ICCV), Seoul, Korea (South), pp.5207-5216, 2019.
|
Z. Wu, C. Shen and A. V. D. Hengel, "Wider or deeper:Revisiting the resnet model for visual recognition", Pattern Recognition, Vol.90, No.C, pp.119-133, 2019.
|