Signal Processing - November 2017 - 49
[11] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman,
"The Pascal visual object classes (VOC) challenge," Int. J. Comput. Vision, vol.
88, no. 2, pp. 303-338, 2010.
[36] D. Pathak, P. Krähenbühl, and T. Darrell, "Constrained convolutional neural
networks for weakly supervised segmentation," in Proc. IEEE Int. Conf.
Computer Vision, 2015, pp. 1742-1750.
[12] P. F. Felzenszwalb and D. P. Huttenlocher, "Efficient graph-based image segmentation," Int. J. Comput. Vision, vol. 59, no. 2, pp. 167-181, 2004.
[37] D. Pathak, E. Shelhamer, J. Long, and T. Darrell, "Fully convolutional multi-class
multiple instance learning," in Proc. Int. Conf. Learning Representations, 2015.
[13] B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik, "Semantic contours from inverse detectors," in Proc. IEEE Int. Conf. Computer Vision, 2011, pp.
991-998.
[38] P. O. Pinheiro and R. Collobert, "From image-level to pixel-level labeling
with convolutional networks," in Proc. IEEE Conf. Computer Vision and Pattern
Recognition, 2015, pp. 1713-1721.
[14] K. He, X. Zhang, S. Ren, and J. Sun, "Delving deep into rectifiers: Surpassing
human-level performance on imagenet classification," in Proc. IEEE Int. Conf.
Computer Vision, 2015, pp. 1026-1034.
[39] A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari, "Learning object
class detectors from weakly annotated video," in Proc. IEEE Conf. Computer
Vision and Pattern Recognition, 2012, pp. 3282-3289.
[15] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2016, pp.
770-778.
[40] X. Qi, Z. Liu, J. Shi, H. Zhao, and J. Jia, "Augmented feedback in semantic
segmentation under image level supervision," in Proc. European Conf. Computer
Vision, 2016, pp. 90-105.
[16] S. Hong, H. Noh, and B. Han, "Decoupled deep neural network for semisupervised semantic segmentation," in Proc. Neural Information Processing
Systems, 2015, pp. 1495-1503.
[41] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once:
Unified, real-time object detection," in Proc. IEEE Conf. Computer Vision and
Pattern Recognition, June 2016., pp. 779-788.
[17] S. Hong, J. Oh, B. Han, and H. Lee, "Learning transferrable knowledge for
semantic segmentation with deep convolutional neural network," in Proc. IEEE
Conf. Computer Vision and Pattern Recognition, 2016, pp. 3204 - 3212.
[42] O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for
biomedical image segmentation," in Proc. Medical Image Computing and
Computer-Assisted Intervention, 2015, pp. 234-241.
[18] S. Hong, D. Yeo, S. Kwak, H. Lee, and B. Han, "Weakly supervised semantic
segmentation using web-crawled videos," in Proc. IEEE Conf. Computer Vision
and Pattern Recognition, 2017, pp. 7322-7330.
[43] C. Rother, V. Kolmogorov, and A. Blake "Grabcut": Interactive foreground
extraction using iterated graph cuts," in Proc. SIGGRAPH, 2004, pp. 309-314.
[19] Z. Jia, X. Huang, E. I. Chang, and Y. Xu, "Constrained deep weak supervision for histopathology image segmentation," arXiv Preprint, arXiv:1701.00794,
2017.
[20] A. Khoreva, R. Benenson, J. Hosang, M. Hein, and B. Schiele, "Exploiting
saliency for object segmentation from image level labels," in Proc. IEEE Conf.
Computer Vision and Pattern Recognition, 2017, pp. 876-885.
[21] A. Kolesnikov and C. H. Lampert, "Improving weakly-supervised object
localization by micro-annotation," in Proc. British Machine Vision Conf., 2016,
pp. 92.1-92.12.
[22] A. Kolesnikov and C. H. Lampert, "Seed, expand and constrain: Three principles for weakly-supervised image segmentation," in Proc. European Conf.
Computer Vision, 2016, pp. 695-711.
[23] P. Krähenbühl and V. Koltun, "Efficient inference in fully connected CRFs
with Gaussian edge potentials," in Proc. Neural Information Processing Systems,
2011, pp. 109-117.
[24] S. Kwak, S. Hong, and B. Han, "Weakly supervised semantic segmentation
using superpixel pooling network," in Proc. AAAI Conf. Artificial Intelligence,
2017, pp. 4111-4117.
[25] Y. Lecun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no.
7553, pp. 436-444, 2015.
[26] D. Lin, J. Dai, J. Jia, K. He, and J. Sun, "Scribblesup: Scribble-supervised
convolutional networks for semantic segmentation," in Proc. IEEE Conf.
Computer Vision and Pattern Recognition, 2016.
[27] G. Lin, C. Shen, A. van dan Hengel, and I. Reid, "Efficient piecewise training
of deep structured models for semantic segmentation," in Proc. IEEE Conf.
Computer Vision and Pattern Recognition, 2016, pp. 3194-3203.
[28] L. Lin, G. Wang, R. Zhang, R. Zhang, X. Liang, and W. Zuo, "Deep structured scene parsing by learning with image description," in Proc. IEEE Conf.
Computer Vision and Pattern Recognition, 2016, pp. 2276-2284.
[29] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár,
and C. L. Zitnick, "Microsoft COCO: Common objects in context," in Proc.
European Conf. Computer Vision, 2014, pp. 740-755.
[30] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C.
Berg, "SSD: Single shot multibox detector," in Proc. European Conf. Computer
Vision, 2016, pp. 21-37.
[44] F. Saleh, M. S. A. Akbarian, M. Salzmann, L. Petersson, S. Gould, and J.
M. Alvarez, "Built-in foreground/background prior for weakly-supervised semantic segmentation," in Proc. European Conf. Computer Vision, 2016, pp. 413-432.
[45] F. Schroff, D. Kalenichenko, and J. Philbin, "Facenet: A unified embedding
for face recognition and clustering," in Proc. IEEE Conf. Computer Vision and
Pattern Recognition, June 2015, pp. 815-823.
[46] W. Shimoda and K. Yanai, "Distinct class-specific saliency maps for weakly
supervised semantic segmentation," in Proc. European Conf. Computer Vision,
2016, pp. 218-234.
[47] J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller, "Striving
for simplicity: The all convolutional net," in Proc. Int. Conf. Learning
Representations, 2015.
[48] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, "Deepface: Closing the gap
to human-level performance in face verification," in Proc. IEEE Conf. Computer
Vision and Pattern Recognition, June 2014, pp. 1701-1708.
[49] P. Tokmakov, K. Alahari, and C. Schmid, "Learning semantic segmentation with
weakly-annotated videos," in Proc. European Conf. Computer Vision, 2016, pp. 388-404.
[50] A. Vezhnevets and J. M. Buhmann, "Towards weakly supervised semantic
segmentation by means of multiple instance and multitask learning," in Proc.
IEEE Conf. on Computer Vision and Pattern Recognition, 2010, pp. 3249-3256.
[51] A. Vezhnevets, V. Ferrari, and J. M. Buhmann, "Weakly supervised semantic
segmentation with a multi-image model," in Proc. IEEE Int. Conf. Computer
Vision, pp. 643-650, 2011.
[52] A. Vezhnevets, V. Ferrari, and J. M. Buhmann, "Weakly supervised structured output learning for semantic segmentation," in Proc. IEEE Conf. Computer
Vision and Pattern Recognition, 2012, pp. 845-852.
[53] S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh, "Convolutional pose
machines," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, June
2016, pp. 4724-4732.
[54] Y. Wei, X. Liang, Y. Chen, X. Shen, M. M. Cheng, J. Feng, Y. Zhao, and S.
Yan, "STC: A simple to complex framework for weakly-supervised semantic segmentation," IEEE Trans. Pattern Anal. Mach. Intell., 2016.
[55] Y. Xu, J.-Y. Zhu, E. I. Chang, M. Lai, and Z. Tu, "Weakly supervised histopathology cancer image segmentation and classification," Med. Image Anal., vol.
18, no. 3, pp. 591-604, 2014.
[31] Z. Liu, X. Li, P. Luo, C. C. Loy, and X. Tang, "Semantic image segmentation
via deep parsing network," in Proc. IEEE Int. Conf. Computer Vision, 2015, pp.
1377-1385.
[56] L. Zhang, M. Song, Z. Liu, X. Liu, J. Bu, and C. Chen, "Probabilistic
graphlet cut: Exploiting spatial structure cue for weakly supervised image segmentation," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2013,
pp. 1908-1915.
[32] J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for
semantic segmentation," in Proc. IEEE Conf. Computer Vision and Pattern
Recognition, 2015, pp. 3431-3440.
[57] S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C.
Huang, and P. Torr, "Conditional random fields as recurrent neural networks," in
Proc. IEEE Int. Conf. Computer Vision, 2015, pp. 1529-1537.
[33] H. Noh, S. Hong, and B. Han, "Learning deconvolution network for semantic segmentation," in Proc. IEEE Int. Conf. Computer Vision, 2015, pp. 1520-
1528.
[58] B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, "Learning
deep features for discriminative localization," in Proc. IEEE Conf. Computer
Vision and Pattern Recognition, 2016, pp. 2921-2929.
[34] S. J. Oh, R. Benenson, A. Khoreva, Z. Akata, M. Fritz, and B. Schiele,
"Exploiting saliency for object segmentation from image level labels," in Proc. IEEE
Conf. Computer Vision and Pattern Recognition, 2017, pp. 4410-4419.
[59] B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba, "Scene
parsing through ade20k dataset," in Proc. IEEE Conf. Computer Vision and
Pattern Recognition, 2017, pp. 633-641.
[35] G. Papandreou, L.-C. Chen, K. Murphy, and A. L. Yuille, "Weakly-and
semi-supervised learning of a DCNN for semantic image segmentation," in Proc.
IEEE Int. Conf. Computer Vision, 2015, pp. 1742-1750.
[60] L. Zitnick and P. Dollar,"Edge boxes: Locating object proposals from
edges," in Proc. European Conf. Computer Vision, 2014, pp. 391-405.
SP
IEEE SIGNAL PROCESSING MAGAZINE
|
November 2017
|
49
Table of Contents for the Digital Edition of Signal Processing - November 2017
Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
Signal Processing - November 2017 - 2
Signal Processing - November 2017 - 3
Signal Processing - November 2017 - 4
Signal Processing - November 2017 - 5
Signal Processing - November 2017 - 6
Signal Processing - November 2017 - 7
Signal Processing - November 2017 - 8
Signal Processing - November 2017 - 9
Signal Processing - November 2017 - 10
Signal Processing - November 2017 - 11
Signal Processing - November 2017 - 12
Signal Processing - November 2017 - 13
Signal Processing - November 2017 - 14
Signal Processing - November 2017 - 15
Signal Processing - November 2017 - 16
Signal Processing - November 2017 - 17
Signal Processing - November 2017 - 18
Signal Processing - November 2017 - 19
Signal Processing - November 2017 - 20
Signal Processing - November 2017 - 21
Signal Processing - November 2017 - 22
Signal Processing - November 2017 - 23
Signal Processing - November 2017 - 24
Signal Processing - November 2017 - 25
Signal Processing - November 2017 - 26
Signal Processing - November 2017 - 27
Signal Processing - November 2017 - 28
Signal Processing - November 2017 - 29
Signal Processing - November 2017 - 30
Signal Processing - November 2017 - 31
Signal Processing - November 2017 - 32
Signal Processing - November 2017 - 33
Signal Processing - November 2017 - 34
Signal Processing - November 2017 - 35
Signal Processing - November 2017 - 36
Signal Processing - November 2017 - 37
Signal Processing - November 2017 - 38
Signal Processing - November 2017 - 39
Signal Processing - November 2017 - 40
Signal Processing - November 2017 - 41
Signal Processing - November 2017 - 42
Signal Processing - November 2017 - 43
Signal Processing - November 2017 - 44
Signal Processing - November 2017 - 45
Signal Processing - November 2017 - 46
Signal Processing - November 2017 - 47
Signal Processing - November 2017 - 48
Signal Processing - November 2017 - 49
Signal Processing - November 2017 - 50
Signal Processing - November 2017 - 51
Signal Processing - November 2017 - 52
Signal Processing - November 2017 - 53
Signal Processing - November 2017 - 54
Signal Processing - November 2017 - 55
Signal Processing - November 2017 - 56
Signal Processing - November 2017 - 57
Signal Processing - November 2017 - 58
Signal Processing - November 2017 - 59
Signal Processing - November 2017 - 60
Signal Processing - November 2017 - 61
Signal Processing - November 2017 - 62
Signal Processing - November 2017 - 63
Signal Processing - November 2017 - 64
Signal Processing - November 2017 - 65
Signal Processing - November 2017 - 66
Signal Processing - November 2017 - 67
Signal Processing - November 2017 - 68
Signal Processing - November 2017 - 69
Signal Processing - November 2017 - 70
Signal Processing - November 2017 - 71
Signal Processing - November 2017 - 72
Signal Processing - November 2017 - 73
Signal Processing - November 2017 - 74
Signal Processing - November 2017 - 75
Signal Processing - November 2017 - 76
Signal Processing - November 2017 - 77
Signal Processing - November 2017 - 78
Signal Processing - November 2017 - 79
Signal Processing - November 2017 - 80
Signal Processing - November 2017 - 81
Signal Processing - November 2017 - 82
Signal Processing - November 2017 - 83
Signal Processing - November 2017 - 84
Signal Processing - November 2017 - 85
Signal Processing - November 2017 - 86
Signal Processing - November 2017 - 87
Signal Processing - November 2017 - 88
Signal Processing - November 2017 - 89
Signal Processing - November 2017 - 90
Signal Processing - November 2017 - 91
Signal Processing - November 2017 - 92
Signal Processing - November 2017 - 93
Signal Processing - November 2017 - 94
Signal Processing - November 2017 - 95
Signal Processing - November 2017 - 96
Signal Processing - November 2017 - 97
Signal Processing - November 2017 - 98
Signal Processing - November 2017 - 99
Signal Processing - November 2017 - 100
Signal Processing - November 2017 - 101
Signal Processing - November 2017 - 102
Signal Processing - November 2017 - 103
Signal Processing - November 2017 - 104
Signal Processing - November 2017 - 105
Signal Processing - November 2017 - 106
Signal Processing - November 2017 - 107
Signal Processing - November 2017 - 108
Signal Processing - November 2017 - 109
Signal Processing - November 2017 - 110
Signal Processing - November 2017 - 111
Signal Processing - November 2017 - 112
Signal Processing - November 2017 - 113
Signal Processing - November 2017 - 114
Signal Processing - November 2017 - 115
Signal Processing - November 2017 - 116
Signal Processing - November 2017 - 117
Signal Processing - November 2017 - 118
Signal Processing - November 2017 - 119
Signal Processing - November 2017 - 120
Signal Processing - November 2017 - 121
Signal Processing - November 2017 - 122
Signal Processing - November 2017 - 123
Signal Processing - November 2017 - 124
Signal Processing - November 2017 - 125
Signal Processing - November 2017 - 126
Signal Processing - November 2017 - 127
Signal Processing - November 2017 - 128
Signal Processing - November 2017 - 129
Signal Processing - November 2017 - 130
Signal Processing - November 2017 - 131
Signal Processing - November 2017 - 132
Signal Processing - November 2017 - 133
Signal Processing - November 2017 - 134
Signal Processing - November 2017 - 135
Signal Processing - November 2017 - 136
Signal Processing - November 2017 - 137
Signal Processing - November 2017 - 138
Signal Processing - November 2017 - 139
Signal Processing - November 2017 - 140
Signal Processing - November 2017 - 141
Signal Processing - November 2017 - 142
Signal Processing - November 2017 - 143
Signal Processing - November 2017 - 144
Signal Processing - November 2017 - 145
Signal Processing - November 2017 - 146
Signal Processing - November 2017 - 147
Signal Processing - November 2017 - 148
Signal Processing - November 2017 - 149
Signal Processing - November 2017 - 150
Signal Processing - November 2017 - 151
Signal Processing - November 2017 - 152
Signal Processing - November 2017 - 153
Signal Processing - November 2017 - 154
Signal Processing - November 2017 - 155
Signal Processing - November 2017 - 156
Signal Processing - November 2017 - 157
Signal Processing - November 2017 - 158
Signal Processing - November 2017 - 159
Signal Processing - November 2017 - 160
Signal Processing - November 2017 - 161
Signal Processing - November 2017 - 162
Signal Processing - November 2017 - 163
Signal Processing - November 2017 - 164
Signal Processing - November 2017 - 165
Signal Processing - November 2017 - 166
Signal Processing - November 2017 - 167
Signal Processing - November 2017 - 168
Signal Processing - November 2017 - 169
Signal Processing - November 2017 - 170
Signal Processing - November 2017 - 171
Signal Processing - November 2017 - 172
Signal Processing - November 2017 - 173
Signal Processing - November 2017 - 174
Signal Processing - November 2017 - 175
Signal Processing - November 2017 - 176
Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201809
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201807
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201805
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201803
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201801
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1117
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0917
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0717
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0517
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0317
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0117
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1116
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0916
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0716
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0516
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0316
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0116
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1115
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0915
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0715
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0515
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0315
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0115
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1114
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0914
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0714
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0514
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0314
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0114
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1113
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0913
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0713
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0513
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0313
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0113
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1112
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0912
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0712
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0512
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0312
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0112
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1111
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0911
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0711
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0511
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0311
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0111
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1110
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0910
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0710
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0510
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0310
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0110
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1109
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0909
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0709
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0509
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0309
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0109
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1108
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0908
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0708
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0508
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0308
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0108
https://www.nxtbookmedia.com