8.3.4.2 Neural Networks for Segmentation

Chapter Contents (Back)
Neural Networks. Semantic Segmentation.

Tsao, E.C.K.[Eric Chen-Kuo], Lin, W.C.[Wei-Chung], Chen, C.T.[Chin-Tu],
Constraint satisfaction neural networks for image recognition,
PR(26), No. 4, April 1993, pp. 553-567.
Elsevier DOI 0401
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Earlier: A2, A1, A3:
Constraint Satisfaction Neural Networks for Image Segmentation,
PR(25), No. 7, July 1992, pp. 679-693.
Elsevier DOI BibRef

Chen, K.S., Tsay, D.H., Huang, W.P., Tzeng, Y.C.,
Remote Sensing Image Segmentation Using a Kalman Filter-Trained Neural-Network,
IJIST(7), No. 2, Summer 1996, pp. 141-148. 9607
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Cheng, K.S.[Kuo-Sheng], Lin, J.S.[Jzau-Sheng], Mao, C.W.[Chi-Wu],
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Elsevier DOI 0806
Constraint satisfaction neural networks (CSNN); Segmentation; Texture edge detection; Fuzzy-C means (FCM); Dynamic window BibRef

Sahami, S., Shayesteh, M.G.,
Bi-level image compression technique using neural networks,
IET-IPR(6), No. 5, 2012, pp. 496-506.
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Längkvist, M.[Martin], Kiselev, A.[Andrey], Alirezaie, M.[Marjan], Loutfi, A.[Amy],
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Wei, Y.C.[Yun-Chao], Liang, X.D.[Xiao-Dan], Chen, Y.P.[Yun-Peng], Jie, Z.[Zequn], Xiao, Y.H.[Yan-Hui], Zhao, Y.[Yao], Yan, S.C.[Shui-Cheng],
Learning to segment with image-level annotations,
PR(59), No. 1, 2016, pp. 234-244.
Elsevier DOI 1609
Semantic segmentation. with CNNs BibRef

Wei, Y.C.[Yun-Chao], Liang, X.D.[Xiao-Dan], Chen, Y.P.[Yun-Peng], Shen, X., Cheng, M.M., Feng, J., Zhao, Y.[Yao], Yan, S.C.[Shui-Cheng],
STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation,
PAMI(39), No. 11, November 2017, pp. 2314-2320.
IEEE DOI 1710
Benchmark testing, Image segmentation, Neural networks, Object detection, Semantics, Training, Semantic segmentation, convolutional neural network, weakly-supervised learning BibRef

Wei, Y.C.[Yun-Chao], Feng, J., Liang, X.D.[Xiao-Dan], Cheng, M.M., Zhao, Y.[Yao], Yan, S.C.[Shui-Cheng],
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach,
CVPR17(6488-6496)
IEEE DOI 1711
Head, Heating systems, Image segmentation, Proposals, Semantics, Training BibRef

Dong, L.[Le], Feng, N.[Ning], Zhang, Q.[Qianni],
LSI: Latent semantic inference for natural image segmentation,
PR(59), No. 1, 2016, pp. 282-291.
Elsevier DOI 1609
Image Segmentation BibRef

Lu, Z.W.[Zhi-Wu], Fu, Z.Y.[Zhen-Yong], Xiang, T.[Tao], Han, P.[Peng], Wang, L.W.[Li-Wei], Gao, X.[Xin],
Learning from Weak and Noisy Labels for Semantic Segmentation,
PAMI(39), No. 3, March 2017, pp. 486-500.
IEEE DOI 1702
Computational modeling BibRef

Li, A.[Aoxue], Lu, Z.W.[Zhi-Wu], Wang, L.W.[Li-Wei], Han, P.[Peng], Wen, J.R.[Ji-Rong],
Large-Scale Sparse Learning From Noisy Tags for Semantic Segmentation,
Cyber(48), No. 1, January 2018, pp. 253-263.
IEEE DOI 1801
Image segmentation, Matrix decomposition, Noise measurement, Noise reduction, Semantics, Symmetric matrices, Visualization, semantic segmentation BibRef

Cao, Y., Shen, C., Shen, H.T.,
Exploiting Depth From Single Monocular Images for Object Detection and Semantic Segmentation,
IP(26), No. 2, February 2017, pp. 836-846.
IEEE DOI 1702
estimation theory BibRef

Zhang, M.[Mi], Hu, X.Y.[Xiang-Yun], Zhao, L.[Like], Lv, Y.[Ye], Luo, M.[Min], Pang, S.Y.[Shi-Yan],
Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
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Ghodrati, A.[Amir], Diba, A.[Ali], Pedersoli, M.[Marco], Tuytelaars, T.[Tinne], Van Gool, L.J.[Luc J.],
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers,
IJCV(124), No. 2, September 2017, pp. 115-131.
Springer DOI 1708
BibRef
Earlier:
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers,
ICCV15(2578-2586)
IEEE DOI 1602
Aggregates BibRef

Hong, S., Kwak, S., Han, B.,
Weakly Supervised Learning with Deep Convolutional Neural Networks for Semantic Segmentation: Understanding Semantic Layout of Images with Minimum Human Supervision,
SPMag(34), No. 6, November 2017, pp. 39-49.
IEEE DOI 1712
Benchmark testing, Image recognition, Image segmentation, Machine learning, Neural networks, Semantics, Visualization BibRef

Holliday, A.[Andrew], Barekatain, M.[Mohammadamin], Laurmaa, J.[Johannes], Kandaswamy, C.[Chetak], Prendinger, H.[Helmut],
Speedup of deep learning ensembles for semantic segmentation using a model compression technique,
CVIU(164), No. 1, 2017, pp. 16-26.
Elsevier DOI 1801
Semantic segmentation BibRef

Romera, E., Álvarez, J.M., Bergasa, L.M., Arroyo, R.,
ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation,
ITS(19), No. 1, January 2018, pp. 263-272.
IEEE DOI 1801
Computer architecture, Image segmentation, Kernel, Real-time systems, Semantics, semantic segmentation BibRef

Chen, L.C.[Liang-Chieh], Papandreou, G.[George], Kokkinos, I.[Iasonas], Murphy, K.P.[Kevin P.], Yuille, A.L.[Alan L.],
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs,
PAMI(40), No. 4, April 2018, pp. 834-848.
IEEE DOI 1804
BibRef
Earlier: A2, A1, A4, A5, Only:
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation,
ICCV15(1742-1750)
IEEE DOI 1602
convolution, feature extraction, feedforward neural nets, image segmentation, learning (artificial intelligence), semantic segmentation. Benchmark testing BibRef

Chen, L.C.[Liang-Chieh], Zhu, Y.K.[Yu-Kun], Papandreou, G.[George], Schroff, F.[Florian], Adam, H.[Hartwig],
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,
ECCV18(VII: 833-851).
Springer DOI 1810
BibRef

Chen, L.C., Barron, J.T., Papandreou, G., Murphy, K.P., Yuille, A.L.,
Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform,
CVPR16(4545-4554)
IEEE DOI 1612
BibRef

Konishi, S.[Scott], Scott Konishi, A., Yuille, A.L.,
Statistical Cues for Domain Specific Image Segmentation with Performance Analysis,
CVPR00(I: 125-132).
IEEE DOI 0005
BibRef

Guo, Y.M.[Yan-Ming], Liu, Y.[Yu], Georgiou, T.[Theodoros], Lew, M.S.[Michael S.],
A review of semantic segmentation using deep neural networks,
MultInfoRetr(8), No. 2, June 2018, pp. 87-93.
Springer DOI 1805
Survey, Semantic Segmentation. BibRef

Nakajima, Y.[Yoshikatsu], Saito, H.[Hideo],
Simultaneous Object Segmentation and Recognition by Merging CNN Outputs from Uniformly Distributed Multiple Viewpoints,
IEICE(E101-D), No. 5, May 2018, pp. 1308-1316.
WWW Link. 1805
BibRef

Wang, C.Y.[Chun-Yan], Xu, A.[Aigong], Li, X.[Xiaoli],
Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
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Wang, C.Y.[Chun-Yan], Xu, A.[Aigong], Li, C.[Chao], Zhao, X.M.[Xue-Mei],
Interval Type-2 Fuzzy Based Neural Network For High Resolution Remote Sensing Image Segmentation,
ISPRS16(B7: 385-391).
DOI Link 1610
BibRef

Liu, Z.W.[Zi-Wei], Li, X.X.[Xiao-Xiao], Luo, P.[Ping], Loy, C.C.[Chen Change], Tang, X.[Xiaoou],
Deep Learning Markov Random Field for Semantic Segmentation,
PAMI(40), No. 8, August 2018, pp. 1814-1828.
IEEE DOI 1807
BibRef
Earlier: A2, A1, A3, A4, A5:
Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade,
CVPR17(6459-6468)
IEEE DOI 1711
BibRef
Earlier: A1, A2, A3, A4, A5:
Semantic Image Segmentation via Deep Parsing Network,
ICCV15(1377-1385)
IEEE DOI 1602
Computational modeling, Computer architecture, Image segmentation, Markov random fields, Neural networks, convolutional neural network. Adaptation models, Cows, Real-time systems, Semantics, Training. Computational efficiency BibRef

Kang, B., Lee, Y., Nguyen, T.Q.,
Depth-Adaptive Deep Neural Network for Semantic Segmentation,
MultMed(20), No. 9, September 2018, pp. 2478-2490.
IEEE DOI 1809
convolution, feedforward neural nets, image colour analysis, image segmentation, learning (artificial intelligence), deep learning BibRef

Chen, T., Lin, L., Wu, X., Xiao, N., Luo, X.,
Learning to Segment Object Candidates via Recursive Neural Networks,
IP(27), No. 12, December 2018, pp. 5827-5839.
IEEE DOI 1810
Proposals, Merging, Semantics, Feature extraction, Neural networks, Measurement, Image segmentation, Object proposal generation, deep learning BibRef

Kemker, R.[Ronald], Luu, R., Kanan, C.[Christopher],
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery,
GeoRS(56), No. 10, October 2018, pp. 6214-6223.
IEEE DOI 1810
Feature extraction, Semantics, Image segmentation, Remote sensing, Image reconstruction, Data models, Support vector machines, semisupervised BibRef

Kemker, R.[Ronald], Salvaggio, C.[Carl], Kanan, C.[Christopher],
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning,
PandRS(145), 2018, pp. 60-77.
Elsevier DOI 1810
Deep learning, Convolutional neural network, Semantic segmentation, Multispectral, Unmanned aerial system, Synthetic imagery BibRef

Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.,
DRINet for Medical Image Segmentation,
MedImg(37), No. 11, November 2018, pp. 2453-2462.
IEEE DOI 1811
Image segmentation, Computer architecture, Convolution, Training, Medical diagnostic imaging, Standards, abdominal organ segmentation BibRef

Larsson, M.[Mĺns], Arnab, A.[Anurag], Zheng, S.[Shuai], Torr, P.H.S.[Philip H.S.], Kahl, F.[Fredrik],
Revisiting Deep Structured Models for Pixel-Level Labeling with Gradient-Based Inference,
SIIMS(11), No. 4, 2018, pp. 2610-2628.
DOI Link 1901
BibRef

Zhu, X.O.[Xia-Obin], Zhang, X.M.[Xin-Ming], Zhang, X.Y.[Xiao-Yu], Xue, Z.[Ziyu], Wang, L.[Lei],
A novel framework for semantic segmentation with generative adversarial network,
JVCIR(58), 2019, pp. 532-543.
Elsevier DOI 1901
Semantic segmentation, Generative adversarial network (GAN), Wasserstein distance, Auxiliary higher-order potential loss BibRef

Roy, A.G.[Abhijit Guha], Navab, N.[Nassir], Wachinger, C.[Christian],
Recalibrating Fully Convolutional Networks With Spatial and Channel 'Squeeze and Excitation' Blocks,
MedImg(38), No. 2, February 2019, pp. 540-549.
IEEE DOI 1902
Image segmentation, Biomedical imaging, Decoding, Task analysis, Encoding, Computer architecture, Retina, squeeze & excitation BibRef

Papadomanolaki, M.[Maria], Vakalopoulou, M.[Maria], Karantzalos, K.[Konstantinos],
A Novel Object-Based Deep Learning Framework for Semantic Segmentation of Very High-Resolution Remote Sensing Data: Comparison with Convolutional and Fully Convolutional Networks,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
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Zhang, R.[Ruimao], Yang, W.[Wei], Peng, Z.L.[Zhang-Lin], Wei, P.X.[Peng-Xu], Wang, X.G.[Xiao-Gang], Lin, L.[Liang],
Progressively diffused networks for semantic visual parsing,
PR(90), 2019, pp. 78-86.
Elsevier DOI 1903
Visual understanding, Image segmentation, Recurrent neural networks, Representation learning BibRef

Li, Y., Guo, Y., Guo, J., Ma, Z., Kong, X., Liu, Q.,
Joint CRF and Locality-Consistent Dictionary Learning for Semantic Segmentation,
MultMed(21), No. 4, April 2019, pp. 875-886.
IEEE DOI 1903
Dictionaries, Machine learning, Image segmentation, Semantics, Task analysis, Inference algorithms, Shape, locality consistency BibRef

Wu, G.M.[Guang-Ming], Guo, Y.M.[Yi-Min], Song, X.[Xiaoya], Guo, Z.L.[Zhi-Ling], Zhang, H.[Haoran], Shi, X.D.[Xiao-Dan], Shibasaki, R.[Ryosuke], Shao, X.W.[Xiao-Wei],
A Stacked Fully Convolutional Networks with Feature Alignment Framework for Multi-Label Land-cover Segmentation,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
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Wang, Q., Yuan, C., Liu, Y.,
Learning Deep Conditional Neural Network for Image Segmentation,
MultMed(21), No. 7, July 2019, pp. 1839-1852.
IEEE DOI 1906
Feature extraction, Object segmentation, Visualization, Brain modeling, Context modeling, Convolutional neural networks, conditional Boltzmann machines BibRef

Benjdira, B.[Bilel], Bazi, Y.[Yakoub], Koubaa, A.[Anis], Ouni, K.[Kais],
Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906
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He, C.[Chu], Fang, P.[Peizhang], Zhang, Z.[Zhi], Xiong, D.[Dehui], Liao, M.S.[Ming-Sheng],
An End-to-End Conditional Random Fields and Skip-Connected Generative Adversarial Segmentation Network for Remote Sensing Images,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
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Wang, Q., Gao, J., Li, X.,
Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes,
IP(28), No. 9, Sep. 2019, pp. 4376-4386.
IEEE DOI 1908
computer vision, convolutional neural nets, feature extraction, image classification, image segmentation, weakly supervision BibRef

Masouleh, M.K.[Mehdi Khoshboresh], Shah-Hosseini, R.[Reza],
Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery,
PandRS(155), 2019, pp. 172-186.
Elsevier DOI 1908
UAV-based thermal infrared imagery, Ground vehicle, Semantic segmentation, Deep learning, Gaussian-Bernoulli Restricted Boltzmann Machine BibRef

Jing, L., Chen, Y., Tian, Y.,
Coarse-to-Fine Semantic Segmentation From Image-Level Labels,
IP(29), No. 1, 2020, pp. 225-236.
IEEE DOI 1910
convolutional neural nets, graph theory, image classification, image segmentation, learning (artificial intelligence), deep learning BibRef

Nogueira, K., Mura, M.D.[M. Dalla], Chanussot, J., Schwartz, W.R., dos Santos, J.A.,
Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks,
GeoRS(57), No. 10, October 2019, pp. 7503-7520.
IEEE DOI 1910
feature extraction, geophysical image processing, image classification, image representation, image resolution, semantic segmentation BibRef

Pereira, S., Pinto, A., Amorim, J., Ribeiro, A., Alves, V., Silva, C.A.,
Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks,
MedImg(38), No. 12, December 2019, pp. 2914-2925.
IEEE DOI 1912
Image segmentation, Kernel, Semantics, Adaptive systems, Convolutional neural networks, Medical diagnostic imaging, adaptive BibRef

Ghosh, S.[Swarnendu], Das, N.[Nibaran], Das, I.[Ishita], Maulik, U.[Ujjwal],
Understanding Deep Learning Techniques for Image Segmentation,
Surveys(52), No. 4, September 2019, pp. Article No 73.
DOI Link 1912
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Han, Y.M.[Yong-Ming], Zhang, S.[Shuheng], Geng, Z.[Zhiqing], Wei, Q.[Qin], Ouyang, Z.[Zhi],
Level set based shape prior and deep learning for image segmentation,
IET-IPR(14), No. 1, January 2020, pp. 183-191.
DOI Link 1912
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Wang, S.[Sherrie], Chen, W.[William], Xie, S.M.[Sang Michael], Azzari, G.[George], Lobell, D.B.[David B.],
Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001
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Zhang, D.W.[Ding-Wen], Han, J.W.[Jun-Wei], Yang, L.[Le], Xx, D.[Dong],
SPFTN: A Joint Learning Framework for Localizing and Segmenting Objects in Weakly Labeled Videos,
PAMI(42), No. 2, February 2020, pp. 475-489.
IEEE DOI 2001
Videos, Task analysis, Reliability, Supervised learning, Object segmentation, Semantics, Feature extraction, self-paced learning BibRef

Huang, Z., Wang, C., Wang, X., Liu, W., Wang, J.,
Semantic Image Segmentation by Scale-Adaptive Networks,
IP(29), 2020, pp. 2066-2077.
IEEE DOI 2001
Image segmentation, Semantics, Detectors, Training, Lips, Task analysis, Feature extraction, Semantic object parsing, scale adaptive BibRef

Huang, Y., Tang, Z., Chen, D., Su, K., Chen, C.,
Batching Soft IoU for Training Semantic Segmentation Networks,
SPLetters(27), 2020, pp. 66-70.
IEEE DOI 2001
Training, Integrated circuits, Semantics, Measurement, Image segmentation, Predictive models, Data models, semantic segmentation BibRef

Yu, J., Blaschko, M.B.,
The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses,
PAMI(42), No. 3, March 2020, pp. 735-748.
IEEE DOI 2002
Fasteners, Risk management, Optimization, Training, Complexity theory, Task analysis, Indexes, Lovász extension, Jaccard index score BibRef

Berman, M., Triki, A.R., Blaschko, M.B.,
The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks,
CVPR18(4413-4421)
IEEE DOI 1812
Indexes, Loss measurement, Optimization, Image segmentation, Fasteners, Training, Semantics BibRef

López, J.[Josué], Torres, D.[Deni], Santos, S.[Stewart], Atzberger, C.[Clement],
Spectral Imagery Tensor Decomposition for Semantic Segmentation of Remote Sensing Data through Fully Convolutional Networks,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
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Chai, D.F.[Deng-Feng], Newsam, S.[Shawn], Huang, J.F.[Jing-Feng],
Aerial image semantic segmentation using DCNN predicted distance maps,
PandRS(161), 2020, pp. 309-322.
Elsevier DOI 2002
Deep learning, Semantic segmentation, DCNNs, Distance maps, Distance transform BibRef

Zhou, L.[Lei], Kong, X.[Xiangyong], Gong, C.[Chen], Zhang, F.[Fan], Zhang, X.[Xiaoguo],
FC-RCCN: Fully convolutional residual continuous CRF network for semantic segmentation,
PRL(130), 2020, pp. 54-63.
Elsevier DOI 2002
Continuous conditional random field (C-CRF), Semantic segmentation, Unary network, Pairwise network BibRef

Karimi, D., Salcudean, S.E.,
Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks,
MedImg(39), No. 2, February 2020, pp. 499-513.
IEEE DOI 2002
Image segmentation, Biomedical imaging, Training, Sensitivity, convolutional neural networks BibRef

Fu, J.[Jun], Liu, J.[Jing], Li, Y.[Yong], Bao, Y.J.[Yong-Jun], Yan, W.P.[Wei-Peng], Fang, Z.W.[Zhi-Wei], Lu, H.Q.[Han-Qing],
Contextual deconvolution network for semantic segmentation,
PR(101), 2020, pp. 107152.
Elsevier DOI 2003
Semantic segmentation, Deconvolution network, Channel contextual module, Spatial contextual module BibRef

López-Cifuentes, A.[Alejandro], Escudero-Vińolo, M.[Marcos], Bescós, J.[Jesús], García-Martín, Á.[Álvaro],
Semantic-aware scene recognition,
PR(102), 2020, pp. 107256.
Elsevier DOI 2003
Scene recognition, Deep learning, Convolutional neural networks, Semantic segmentation BibRef

Li, K.[Kun], Hu, X.Y.[Xiang-Yun], Jiang, H.[Huiwei], Shu, Z.[Zhen], Zhang, M.[Mi],
Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
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Zhang, P.P.[Ping-Ping], Liu, W.[Wei], Lei, Y.J.[Yin-Jie], Wang, H.Y.[Hong-Yu], Lu, H.C.[Hu-Chuan],
RAPNet: Residual Atrous Pyramid Network for Importance-Aware Street Scene Parsing,
IP(29), 2020, pp. 5010-5021.
IEEE DOI 2003
Semantics, Feature extraction, Machine learning, Labeling, Coherence, Convolution, Autonomous vehicles, Street scene parsing, fully convolutional network BibRef

Jiang, B.[Bin], Tu, W.X.[Wen-Xuan], Yang, C.[Chao], Yuan, J.S.[Jun-Song],
Context-Integrated and Feature-Refined Network for Lightweight Object Parsing,
IP(29), 2020, pp. 5079-5093.
IEEE DOI 2003
Semantics, Image segmentation, Computer architecture, Convolution, Convolutional codes, Computational complexity, multi-scale context information BibRef


Ye, L.W.[Lin-Wei], Rochan, M.[Mrigank], Liu, Z.[Zhi], Wang, Y.[Yang],
Cross-Modal Self-Attention Network for Referring Image Segmentation,
CVPR19(10494-10503).
IEEE DOI 2002
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Cao, J.[Jiale], Pang, Y.W.[Yan-Wei], Li, X.L.[Xue-Long],
Triply Supervised Decoder Networks for Joint Detection and Segmentation,
CVPR19(7384-7393).
IEEE DOI 2002
BibRef

Xiong, Y.[Yuwen], Liao, R.[Renjie], Zhao, H.S.[Heng-Shuang], Hu, R.[Rui], Bai, M.[Min], Yumer, E.[Ersin], Urtasun, R.[Raquel],
UPSNet: A Unified Panoptic Segmentation Network,
CVPR19(8810-8818).
IEEE DOI 2002
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Kirillov, A.[Alexander], He, K.[Kaiming], Girshick, R.[Ross], Rother, C.[Carsten], Dollar, P.[Piotr],
Panoptic Segmentation,
CVPR19(9396-9405).
IEEE DOI 2002
BibRef

Liu, H.[Huanyu], Peng, C.[Chao], Yu, C.Q.[Chang-Qian], Wang, J.[Jingbo], Liu, X.[Xu], Yu, G.[Gang], Jiang, W.[Wei],
An End-To-End Network for Panoptic Segmentation,
CVPR19(6165-6174).
IEEE DOI 2002
BibRef

Li, Y.W.[Yan-Wei], Chen, X.[Xinze], Zhu, Z.[Zheng], Xie, L.X.[Ling-Xi], Huang, G.[Guan], Du, D.L.[Da-Long], Wang, X.G.[Xin-Gang],
Attention-Guided Unified Network for Panoptic Segmentation,
CVPR19(7019-7028).
IEEE DOI 2002
BibRef

Chen, W.Y.[Wu-Yang], Jiang, Z.[Ziyu], Wang, Z.Y.[Zhang-Yang], Cui, K.[Kexin], Qian, X.N.[Xiao-Ning],
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images,
CVPR19(8916-8925).
IEEE DOI 2002
BibRef

Zhang, Y.H.[Yi-Heng], Qiu, Z.[Zhaofan], Liu, J.G.[Jin-Gen], Yao, T.[Ting], Liu, D.[Dong], Mei, T.[Tao],
Customizable Architecture Search for Semantic Segmentation,
CVPR19(11633-11642).
IEEE DOI 2002
BibRef

Nekrasov, V.[Vladimir], Chen, H.[Hao], Shen, C.H.[Chun-Hua], Reid, I.D.[Ian D.],
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells,
CVPR19(9118-9127).
IEEE DOI 2002
BibRef

Lee, J.[Jungbeom], Kim, E.[Eunji], Lee, S.[Sungmin], Lee, J.[Jangho], Yoon, S.[Sungroh],
FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using Stochastic Inference,
CVPR19(5262-5271).
IEEE DOI 2002
BibRef

Mou, L.[Lichao], Hua, Y.[Yuansheng], Zhu, X.X.[Xiao Xiang],
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes,
CVPR19(12408-12417).
IEEE DOI 2002
BibRef

Larsson, M.[Mans], Stenborg, E.[Erik], Hammarstrand, L.[Lars], Pollefeys, M.[Marc], Sattler, T.[Torsten], Kahl, F.[Fredrik],
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation,
CVPR19(9524-9534).
IEEE DOI 2002
BibRef

Tokunaga, H.[Hiroki], Teramoto, Y.[Yuki], Yoshizawa, A.[Akihiko], Bise, R.[Ryoma],
Adaptive Weighting Multi-Field-Of-View CNN for Semantic Segmentation in Pathology,
CVPR19(12589-12598).
IEEE DOI 2002
BibRef

Li, Y.S.[Yun-Sheng], Yuan, L.[Lu], Vasconcelos, N.[Nuno],
Bidirectional Learning for Domain Adaptation of Semantic Segmentation,
CVPR19(6929-6938).
IEEE DOI 2002
BibRef

Wei, Z.[Zhen], Zhang, J.Y.[Jing-Yi], Liu, L.[Li], Zhu, F.[Fan], Shen, F.[Fumin], Zhou, Y.[Yi], Liu, S.[Si], Sun, Y.[Yao], Shao, L.[Ling],
Building Detail-Sensitive Semantic Segmentation Networks With Polynomial Pooling,
CVPR19(7108-7116).
IEEE DOI 2002
BibRef

He, J.J.[Jun-Jun], Deng, Z.Y.[Zhong-Ying], Zhou, L.[Lei], Wang, Y.[Yali], Qiao, Y.[Yu],
Adaptive Pyramid Context Network for Semantic Segmentation,
CVPR19(7511-7520).
IEEE DOI 2002
BibRef

Li, H.[Hanchao], Xiong, P.F.[Peng-Fei], Fan, H.Q.A.[Hao-Qi-Ang], Sun, J.[Jian],
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation,
CVPR19(9514-9523).
IEEE DOI 2002
BibRef

Sun, R.[Ruoqi], Zhu, X.[Xinge], Wu, C.[Chongruo], Huang, C.[Chen], Shi, J.P.[Jian-Ping], Ma, L.Z.[Li-Zhuang],
Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection,
CVPR19(4355-4364).
IEEE DOI 2002
BibRef

Chen, Y.H.[Yu-Hua], Li, W.[Wen], Chen, X.[Xiaoran], Van Gool, L.J.[Luc J.],
Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach,
CVPR19(1841-1850).
IEEE DOI 2002
BibRef

Durall, R.[Ricard], Pfreundt, F.J.[Franz-Josef], Köthe, U.[Ullrich], Keuper, J.[Janis],
Object Segmentation Using Pixel-Wise Adversarial Loss,
GCPR19(303-316).
Springer DOI 1911
BibRef

Wang, Y., Zhou, Q., Liu, J., Xiong, J., Gao, G., Wu, X., Latecki, L.J.,
Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation,
ICIP19(1860-1864)
IEEE DOI 1910
CNN, Lightweight network, Encoder-decoder network, ResNet, Real-time semantic segmentation BibRef

Xing, Y., Wang, J., Chen, X., Zeng, G.,
Coupling Two-Stream RGB-D Semantic Segmentation Network by Idempotent Mappings,
ICIP19(1850-1854)
IEEE DOI 1910
RGB-D Semantic Segmentation, Convoutional Neural Networks BibRef

Lyu, H., Fu, H., Hu, X., Liu, L.,
Esnet: Edge-Based Segmentation Network for Real-Time Semantic Segmentation in Traffic Scenes,
ICIP19(1855-1859)
IEEE DOI 1910
Real-Time, Semantic Segmentation, Global Edge Information, Classification Level Semantic Information BibRef

Ma, L.Y.[Lei-Yuan], Liu, Z.Y.[Zi-Yi], Zheng, N.N.[Nan-Ning], Wang, J.J.[Jian-Ji],
HAR Enhanced Weakly-Supervised Semantic Segmentation Coupled with Adversarial Learning,
ICIP19(1845-1849)
IEEE DOI 1910
semantic segmentation, weakly-supervised, adversarial learning, atrous rate BibRef

Yokoo, S., Iizuka, S., Fukui, K.,
MLSNet: Resource-Efficient Adaptive Inference with Multi-Level Segmentation Networks,
ICIP19(1510-1514)
IEEE DOI 1910
semantic segmentation, convolutional network, adaptive inference BibRef

Liu, M., Yin, H.,
Cross Attention Network for Semantic Segmentation,
ICIP19(2434-2438)
IEEE DOI 1910
Semantic segmentation, cross attention, real-time, deep neural networks BibRef

Bigdeli, S., Süsstrunk, S.,
Deep Semantic Segmentation Using NIR as Extra Physical Information,
ICIP19(2439-2443)
IEEE DOI 1910
Deep Semantic Segmentation, Near Infrared, Convolutional Neural Networks BibRef

Guo, J., Markoni, H.,
Image Semantic Segmentation With Edge and Feature Level Attenuators,
ICIP19(2511-2515)
IEEE DOI 1910
ENet, skip connection, attenuator, edge selector, image segmentation BibRef

Ganti, P., Waslander, S.,
Network Uncertainty Informed Semantic Feature Selection for Visual SLAM,
CRV19(121-128)
IEEE DOI 1908
Simultaneous localization and mapping, Feature extraction, Uncertainty, Artificial neural networks, Semantics, Entropy, Semantic Segmentation BibRef

Hu, T.[Tao],
Dense In Dense: Training Segmentation from Scratch,
ACCV18(VI:454-470).
Springer DOI 1906
BibRef

Türkmen, S.[Sercan], Heikkilä, J.[Janne],
An Efficient Solution for Semantic Segmentation: ShuffleNet V2 with Atrous Separable Convolutions,
SCIA19(41-53).
Springer DOI 1906
See also ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. BibRef

Kim, Y.[Youngeun], Kim, S.H.[Seung-Hyeon], Kim, T.[Taekyung], Kim, C.[Changick],
CNN-Based Semantic Segmentation Using Level Set Loss,
WACV19(1752-1760)
IEEE DOI 1904
convolutional neural nets, entropy, image resolution, image segmentation, probability, set theory, level set loss, Training BibRef

Xiang, W., Mao, H., Athitsos, V.,
ThunderNet: A Turbo Unified Network for Real-Time Semantic Segmentation,
WACV19(1789-1796)
IEEE DOI 1904
embedded systems, graphics processing units, image segmentation, neural nets, Turbo Unified Network, ThunderNet, Standards BibRef

Pandey, G., Dukkipati, A.,
Learning to Segment With Image-Level Supervision,
WACV19(1856-1865)
IEEE DOI 1904
convolution, image classification, image representation, image segmentation, learning (artificial intelligence), Force BibRef

Karim, R., Islam, M.A., Bruce, N.D.B.,
Recurrent Iterative Gating Networks for Semantic Segmentation,
WACV19(1070-1079)
IEEE DOI 1904
image segmentation, iterative methods, learning (artificial intelligence), neural net architecture. BibRef

Zhang, L.[Liang], Kong, X.W.[Xiang-Wen], Shen, P.Y.[Pei-Yi], Zhu, G.M.[Guang-Ming], Song, J.[Juan], Shah, S.A.A.[Syed Afaq Ali], Bennamoun, M.[Mohammed],
Reflective Field for Pixel-Level Tasks,
ICPR18(529-534)
IEEE DOI 1812
Task analysis, Kernel, Computer architecture, Convolution, Semantics, Neural networks, Image segmentation BibRef

Ortiz, A., Granados, A., Fuentes, O., Kiekintveld, C., Rosario, D., Bell, Z.,
Integrated Learning and Feature Selection for Deep Neural Networks in Multispectral Images,
PBVS18(1277-127709)
IEEE DOI 1812
Image segmentation, Semantics, Machine learning, Training, Task analysis, Feature extraction, Neural networks BibRef

McIntosh, L., Maheswaranathan, N., Sussillo, D., Shlens, J.,
Recurrent Segmentation for Variable Computational Budgets,
EfficientDeep18(1729-172909)
IEEE DOI 1812
Image segmentation, Semantics, Computer architecture, Computational efficiency, Videos, Computational modeling, Network architecture BibRef

Xu, X., Lu, Q., Yang, L., Hu, S., Chen, D., Hu, Y., Shi, Y.,
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation,
CVPR18(8300-8308)
IEEE DOI 1812
Quantization (signal), Training, Biomedical imaging, Image segmentation, Uncertainty, Memory management, Neural networks BibRef

Li, R., Li, K., Kuo, Y., Shu, M., Qi, X., Shen, X., Jia, J.,
Referring Image Segmentation via Recurrent Refinement Networks,
CVPR18(5745-5753)
IEEE DOI 1812
Image segmentation, Semantics, Natural languages, Task analysis, Feature extraction, Logic gates, Training BibRef

Briot, A., Viswanath, P., Yogamani, S.,
Analysis of Efficient CNN Design Techniques for Semantic Segmentation,
ECVW18(776-77609)
IEEE DOI 1812
Convolution, Computer architecture, Semantics, Hardware, Quantization (signal), Kernel, Computational modeling BibRef

Sankaranarayanan, S., Balaji, Y., Jain, A., Lim, S.N., Chellappa, R.,
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation,
CVPR18(3752-3761)
IEEE DOI 1812
Task analysis, Semantics, Training, Image reconstruction, Generators, Image segmentation, Data models BibRef

Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.,
Learning a Discriminative Feature Network for Semantic Segmentation,
CVPR18(1857-1866)
IEEE DOI 1812
Semantics, Task analysis, Feature extraction, Convolution, Computer architecture, Computer vision, Benchmark testing BibRef

Arnab, A., Miksik, O., Torr, P.H.S.,
On the Robustness of Semantic Segmentation Models to Adversarial Attacks,
CVPR18(888-897)
IEEE DOI 1812
Robustness, Semantics, Image segmentation, Perturbation methods, Task analysis, Neural networks, Training BibRef

Wang, X., You, S., Li, X., Ma, H.,
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features,
CVPR18(1354-1362)
IEEE DOI 1812
Image segmentation, Semantics, Training, Heating systems, Task analysis, Feature extraction, Computer vision BibRef

Shen, T., Lin, G., Shen, C., Reid, I.D.,
Bootstrapping the Performance of Webly Supervised Semantic Segmentation,
CVPR18(1363-1371)
IEEE DOI 1812
Training, Image segmentation, Semantics, Knowledge engineering, Noise measurement, Bidirectional control, Estimation BibRef

Zhang, Z., Xie, C., Wang, J., Xie, L., Yuille, A.L.,
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection Under Partial Occlusion,
CVPR18(1372-1380)
IEEE DOI 1812
Semantics, Visualization, Training, Wheels, Proposals, Feature extraction, Object detection BibRef

Zlateski, A., Jaroensri, R., Sharma, P., Durand, F.,
On the Importance of Label Quality for Semantic Segmentation,
CVPR18(1479-1487)
IEEE DOI 1812
Computer vision, Pattern recognition BibRef

Zhang, Y., Qiu, Z., Yao, T., Liu, D., Mei, T.,
Fully Convolutional Adaptation Networks for Semantic Segmentation,
CVPR18(6810-6818)
IEEE DOI 1812
Semantics, Image segmentation, Adaptation models, Visualization, Task analysis, Games, Videos BibRef

Saleh, F.S.[Fatemeh Sadat], Aliakbarian, M.S.[Mohammad Sadegh], Salzmann, M.[Mathieu], Petersson, L.[Lars], Alvarez, J.M.[Jose M.],
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation,
ECCV18(II: 86-103).
Springer DOI 1810
BibRef

Marin, D.[Dmitrii], Tang, M.[Meng], Ben Ayed, I.[Ismail], Boykov, Y.[Yuri],
Beyond Gradient Descent for Regularized Segmentation Losses,
CVPR19(10179-10188).
IEEE DOI 2002
BibRef

Tang, M.[Meng], Perazzi, F.[Federico], Djelouah, A.[Abdelaziz], Ben Ayed, I.[Ismail], Schroers, C.[Christopher], Boykov, Y.[Yuri],
On Regularized Losses for Weakly-supervised CNN Segmentation,
ECCV18(XVI: 524-540).
Springer DOI 1810
BibRef

Hu, T.[Tao], Wang, Y.[Yao], Chen, Y.S.[Yi-Song], Lu, P.[Peng], Wang, H.[Heng], Wang, G.P.[Guo-Ping],
Sobel Heuristic Kernel for Aerial Semantic Segmentation,
ICIP18(3074-3078)
IEEE DOI 1809
Kernel, Semantics, Image segmentation, Image edge detection, Neural networks, Detectors, Convolution, Semantic Segmentation, Edge Detection BibRef

Siam, M., Gamal, M., Abdel-Razek, M., Yogamani, S., Jagersand, M., Zhang, H.,
A Comparative Study of Real-Time Semantic Segmentation for Autonomous Driving,
ECVW18(700-70010)
IEEE DOI 1812
Convolution, Semantics, Computer architecture, Decoding, Context modeling, Real-time systems, Image segmentation BibRef

Siam, M., Gamal, M., Abdel-Razek, M., Yogamani, S., Jagersand, M.,
RTSeg: Real-Time Semantic Segmentation Comparative Study,
ICIP18(1603-1607)
IEEE DOI 1809
Computer architecture, Convolution, Semantics, Decoding, Feature extraction, Benchmark testing, Real-time systems, realtime, benchmarking framework BibRef

Feng, Z., Yong, H., Xukun, S.,
GRANet: Global Refinement Atrous Convolutional Neural Network for Semantic Scene Segmentation,
ICIP18(1568-1572)
IEEE DOI 1809
Semantics, Feature extraction, Convolution, Image segmentation, Task analysis, Training, Convolutional neural networks, Global Context BibRef

Yang, W., Zhou, Q., Lu, J., Wu, X., Zhang, S., Latecki, L.J.,
Dense Deconvolutional Network for Semantic Segmentation,
ICIP18(1573-1577)
IEEE DOI 1809
Image segmentation, Training, Semantics, Decoding, Convolution, Deconvolution, Feature extraction, Semantic Segmentation, FCNs BibRef

Oudni, L., Vázquez, C., Coulombe, S.,
Motion Occlusions for Automatic Generation of Relative Depth Maps,
ICIP18(1538-1542)
IEEE DOI 1809
Optical imaging, Integrated optics, Image color analysis, Estimation, Coherence, Interpolation, Image segmentation, BibRef

Huang, Q., Xia, C., Li, S., Wang, Y., Song, Y., Kuo, C.C.J.,
Unsupervised Clustering Guided Semantic Segmentation,
WACV18(1489-1498)
IEEE DOI 1806
feature extraction, feedforward neural nets, image classification, image representation, image segmentation, Training BibRef

Nigam, I., Huang, C., Ramanan, D.,
Ensemble Knowledge Transfer for Semantic Segmentation,
WACV18(1499-1508)
IEEE DOI 1806
image segmentation, learning (artificial intelligence), aerial drone robotics, aerial scenes, aerial segmentation, Visualization BibRef

Richmond, D.[David], Kainmueller, D.[Dagmar], Yang, M.[Michael], Myers, E.[Eugene], Rother, C.[Carsten],
Mapping Auto-context Decision Forests to Deep ConvNets for Semantic Segmentation,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Nekrasov, V.[Vladimir], Ju, J.[Janghoon], Choi, J.[Jaesik],
Global Deconvolutional Networks for Semantic Segmentation,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Jiang, Y., Chi, Z.,
A Fully-Convolutional Framework for Semantic Segmentation,
DICTA17(1-7)
IEEE DOI 1804
image classification, image segmentation, learning (artificial intelligence), deep learning technique, Semantics BibRef

Fu, J., Liu, J., Wang, Y., Lu, H.,
Densely connected deconvolutional network for semantic segmentation,
ICIP17(3085-3089)
IEEE DOI 1803
Convergence, Image segmentation, Semantics, Spatial resolution, Stacking, Training, Deconvolutional Network, Dense Connection, Semantic Segmentation BibRef

Chu, J., Xiao, X., Meng, G., Wang, L., Pan, C.,
Learnable contextual regularization for semantic segmentation of indoor scene images,
ICIP17(1267-1271)
IEEE DOI 1803
Computer architecture, Convolution, Image segmentation, Kernel, Semantics, Task analysis, Training, Contextual constraints, Semantic segmentation BibRef

Liu, Y., Lew, M.S.,
Improving the discrimination between foreground and background for semantic segmentation,
ICIP17(1272-1276)
IEEE DOI 1803
Computational modeling, Image segmentation, Proposals, Semantics, Standards, Task analysis, Training, Fully Convolutional Networks, Semantic Segmentation BibRef

Qi, X., Liao, R., Jia, J., Fidler, S., Urtasun, R.,
3D Graph Neural Networks for RGBD Semantic Segmentation,
ICCV17(5209-5218)
IEEE DOI 1802
feature extraction, graph theory, image classification, image representation, image segmentation, BibRef

Souly, N., Spampinato, C., Shah, M.,
Semi Supervised Semantic Segmentation Using Generative Adversarial Network,
ICCV17(5689-5697)
IEEE DOI 1802
feature extraction, image classification, image segmentation, learning (artificial intelligence), semantic networks, Visualization BibRef

Sickert, S.[Sven], Denzler, J.[Joachim],
Semantic Segmentation of Outdoor Areas Using 3D Moment Invariants and Contextual Cues,
GCPR17(165-176).
Springer DOI 1711
BibRef

Morley, D., Foroosh, H.,
Improving RANSAC-Based Segmentation through CNN Encapsulation,
CVPR17(2661-2670)
IEEE DOI 1711
Encapsulation, Feature extraction, Image edge detection, Image segmentation, Training BibRef

Ke, T.W., Maire, M., Yu, S.X.,
Multigrid Neural Architectures,
CVPR17(4067-4075)
IEEE DOI 1711
Computer architecture, Convolution, Image segmentation, Routing, Semantics, Standards BibRef

Cohen, G., Weinshall, D.,
Hidden Layers in Perceptual Learning,
CVPR17(5349-5357)
IEEE DOI 1711
Biological system modeling, Computational modeling, Convolution, Image segmentation, Training, Visualization BibRef

Roy, A.[Anirban], Todorovic, S.[Sinisa],
Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation,
CVPR17(7282-7291)
IEEE DOI 1711
BibRef
Earlier:
A Multi-scale CNN for Affordance Segmentation in RGB Images,
ECCV16(IV: 186-201).
Springer DOI 1611
Gaussian distribution, Image segmentation, Labeling, Neurons, Semantics, Training, Visualization BibRef

Nissen, M.S.[Malte S.], Krause, O.[Oswin], Almstrup, K.[Kristian], Kjćrulff, S.[Sřren], Nielsen, T.T.[Torben T.], Nielsen, M.[Mads],
Convolutional Neural Networks for Segmentation and Object Detection of Human Semen,
SCIA17(I: 397-406).
Springer DOI 1706
BibRef

Schneider, L.[Lukas], Jasch, M.[Manuel], Fröhlich, B.[Björn], Weber, T.[Thomas], Franke, U.[Uwe], Pollefeys, M.[Marc], Rätsch, M.[Matthias],
Multimodal Neural Networks: RGB-D for Semantic Segmentation and Object Detection,
SCIA17(I: 98-109).
Springer DOI 1706
BibRef

García, G.M., Husain, F., Schulz, H., Frintrop, S., Torras, C., Behnke, S.,
Semantic segmentation priors for object discovery,
ICPR16(549-554)
IEEE DOI 1705
Computer vision, Image color analysis, Image segmentation, Neural networks, Proposals, Semantics BibRef

Wang, C., Yu, J., Mauch, L., Yang, B.,
Binary Segmentation Based Class Extension in Semantic Image Segmentation Using Convolutional Neural Networks,
ICIP18(2232-2236)
IEEE DOI 1809
Image segmentation, Semantics, Training, Task analysis, Computational modeling, Manuals, Convolutional neural networks, convolutional neural networks BibRef

Wang, C., Mauch, L., Guo, Z., Yang, B.,
On semantic image segmentation using deep convolutional neural network with shortcuts and easy class extension,
IPTA16(1-6)
IEEE DOI 1703
image segmentation BibRef

Mousavian, A., Pirsiavash, H., KošeckŽá, J.,
Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks,
3DV16(611-619)
IEEE DOI 1701
Computer architecture BibRef

Cannici, M.[Marco], Ciccone, M.[Marco], Romanoni, A.[Andrea], Matteucci, M.[Matteo],
Attention Mechanisms for Object Recognition With Event-Based Cameras,
WACV19(1127-1136)
IEEE DOI 1904
cameras, image recognition, image sequences, neural nets, object recognition, object recognition, event-based cameras, Object recognition BibRef

Visin, F., Romero, A., Cho, K., Matteucci, M., Ciccone, M., Kastner, K., Bengio, Y., Courville, A.,
ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation,
DeepLearn-C16(426-433)
IEEE DOI 1612
BibRef

Jarrar, M., Kerkeni, A., Abdallah, A.B., Bedoui, M.H.,
MLP Neural Network Classifier for Medical Image Segmentation,
CGiV16(88-93)
IEEE DOI 1608
image classification BibRef

Hernández, J.[Juanita], Gómez, W.[Wilfrido],
Automatic Tuning of the Pulse-Coupled Neural Network Using Differential Evolution for Image Segmentation,
MCPR16(157-166).
Springer DOI 1608
BibRef

Murdock, C.[Calvin], de la Torre, F.[Fernando],
Additive Component Analysis,
CVPR17(673-681)
IEEE DOI 1711
BibRef
Earlier:
Semantic Component Analysis,
ICCV15(1484-1492)
IEEE DOI 1602
Additives, Image reconstruction, Kernel, Machine learning, Manifolds, Optimization, Principal component analysis. Feature extraction. decomposition of images into semantic components. BibRef

Noh, H., Hong, S., Han, B.,
Learning Deconvolution Network for Semantic Segmentation,
ICCV15(1520-1528)
IEEE DOI 1602
Deconvolution BibRef

Lin, D., Dai, J., Jia, J., He, K., Sun, J.,
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation,
CVPR16(3159-3167)
IEEE DOI 1612
BibRef

Dai, J., He, K., Sun, J.,
Instance-Aware Semantic Segmentation via Multi-task Network Cascades,
CVPR16(3150-3158)
IEEE DOI 1612
BibRef
And:
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation,
ICCV15(1635-1643)
IEEE DOI 1602
Erbium BibRef

Pathak, D., Krahenbuhl, P., Darrell, T.J.,
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation,
ICCV15(1796-1804)
IEEE DOI 1602
Convolutional codes BibRef

Safar, S.[Simon], Yang, M.H.[Ming-Hsuan],
Learning shape priors for object segmentation via neural networks,
ICIP15(1835-1839)
IEEE DOI 1512
Object segmentation; convolutional neural networks; shape priors BibRef

Gidaris, S.[Spyridon], Komodakis, N.[Nikos],
Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization,
BMVC16(xx-yy).
HTML Version. 1805
BibRef
And:
LocNet: Improving Localization Accuracy for Object Detection,
CVPR16(789-798)
IEEE DOI 1612
BibRef
Earlier:
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model,
ICCV15(1134-1142)
IEEE DOI 1602
Biological system modeling BibRef

Porzi, L.[Lorenzo], Rota Bulo, S.[Samuel], Colovic, A.[Aleksander], Kontschieder, P.[Peter],
Seamless Scene Segmentation,
CVPR19(8269-8278).
IEEE DOI 2002
BibRef

Rota Bulo, S.[Samuel], Kontschieder, P.[Peter],
Neural Decision Forests for Semantic Image Labelling,
CVPR14(81-88)
IEEE DOI 1409
neural network; random forest; semantic image labelling BibRef

Masci, J.[Jonathan], Giusti, A.[Alessandro], Ciresan, D.C.[Dan C.], Fricout, G.[Gabriel], Schmidhuber, J.[Jurgen],
A fast learning algorithm for image segmentation with max-pooling convolutional networks,
ICIP13(2713-2717)
IEEE DOI 1402
Convolutional Network BibRef

Giusti, A.[Alessandro], Ciresan, D.C.[Dan C.], Masci, J.[Jonathan], Gambardella, L.M.[Luca M.], Schmidhuber, J.[Jurgen],
Fast image scanning with deep max-pooling convolutional neural networks,
ICIP13(4034-4038)
IEEE DOI 1402
Biomedical Imaging BibRef

del Campo-Becerra, G.D.M.[Gustavo D. Martín], Yańez-Vargas, J.I.[Juan I.], López-Ruíz, J.A.[Josué A.],
Texture Analysis of Mean Shift Segmented Low-Resolution Speckle-Corrupted Fractional SAR Imagery through Neural Network Classification,
CASI14(998-1005).
Springer DOI 1411
BibRef

Yazdanpanah, A.P.[Ali Pour], Regentova, E.E.[Emma E.], Mandava, A.K.[Ajay Kumar], Ahmad, T.[Touqeer],
Sky Segmentation by Fusing Clustering with Neural Networks,
ISVC13(II:663-672).
Springer DOI 1311
BibRef

Andersen, J.D.[Jens D.],
Image Decomposition by Radial Basis Functions,
SCIA03(749-754).
Springer DOI 0310
BibRef

Matsui, K.[Kazuhiro], Kosugi, Y.[Yukio],
Image Segmentation by Neural-net Classifiers with Genetic Selection of Feature Indices,
ICIP99(I:524-528).
IEEE DOI BibRef 9900

Zong, X., Meyer-Baese, A., and Laine, A.,
Multiscale Segmentation Through a Radial Basis Neural Network,
ICIP97(III: 400-403).
IEEE DOI BibRef 9700

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Other Complete Systems .


Last update:Mar 23, 2020 at 19:20:50