8.3.4.3.1 Neural Networks for Semantic Segmentation

Chapter Contents (Back)
Neural Networks. Semantic Segmentation. Detection mostly: See also Convolutional Neural Networks for Object Detection and Segmentation. See also Semantic Segmentation, Label and Segment Together. See also Neural Networks for Segmentation.

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

Wang, H.Z.[Hong-Zhen], Wang, Y.[Ying], Zhang, Q.[Qian], Xiang, S.M.[Shi-Ming], Pan, C.H.[Chun-Hong],
Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Guo, S.C.[Shi-Chen], Jin, Q.Z.[Qi-Zhao], Wang, H.Z.[Hong-Zhen], Wang, X.Z.[Xue-Zhi], Wang, Y.G.[Yan-Gang], Xiang, S.M.[Shi-Ming],
Learnable Gated Convolutional Neural Network for Semantic Segmentation in Remote-Sensing Images,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Shelhamer, E.[Evan], Long, J.[Jonathan], Darrell, T.J.[Trevor J.],
Fully Convolutional Networks for Semantic Segmentation,
PAMI(39), No. 4, April 2017, pp. 640-651.
IEEE DOI 1703
BibRef
Earlier: A2, A1, A3: CVPR15(3431-3440)
IEEE DOI 1510
Award, CVPR, HM. Computer architecture BibRef

Shelhamer, E.[Evan], Rakelly, K.[Kate], Hoffman, J.[Judy], Darrell, T.J.[Trevor J.],
Clockwork Convnets for Video Semantic Segmentation,
VSeg16(III: 852-868).
Springer DOI 1611
BibRef

Liu, Y.[Yu], Nguyen, D.M.[Duc Minh], Deligiannis, N.[Nikos], Ding, W.[Wenrui], Munteanu, A.[Adrian],
Hourglass-Shape Network Based Semantic Segmentation for High Resolution Aerial Imagery,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Badrinarayanan, V.[Vijay], Kendall, A.[Alex], Cipolla, R.[Roberto],
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,
PAMI(39), No. 12, December 2017, pp. 2481-2495.
IEEE DOI 1711
Convolutional codes, Decoding, Image segmentation, Neural networks, Semantics, Training, indoor scenes, pooling, road scenes, semantic pixel-wise segmentation, upsampling BibRef

Cipolla, R.[Roberto], Gal, Y., Kendall, A.[Alex],
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics,
CVPR18(7482-7491)
IEEE DOI 1812
Task analysis, Uncertainty, Semantics, Geometry, Image segmentation, Computational modeling 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
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

Afridi, M.J.[Muhammad Jamal], Ross, A.[Arun], Shapiro, E.M.[Erik M.],
On automated source selection for transfer learning in convolutional neural networks,
PR(73), No. 1, 2018, pp. 65-75.
Elsevier DOI 1709
Transfer learning BibRef

Xu, N.[Nuo], Huo, C.L.[Chun-Lei],
Learning Deep Relationship for Object Detection,
IEICE(E101-D), No. 1, January 2018, pp. 273-276.
WWW Link. 1801
BibRef

Han, J.W.[Jun-Wei], Zhang, D.W.[Ding-Wen], Cheng, G.[Gong], Liu, N.[Nian], Xu, D.[Dong],
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey,
SPMag(35), No. 1, January 2018, pp. 84-100.
IEEE DOI 1801
Survey, Deep Nets. Computer architecture, Computer vision, Convolution, Feature extraction, Machine learning, Object detection, Visualization BibRef

Liu, Y.[Yi], Han, J.G.[Jun-Gong], Zhang, Q.[Qiang], Shan, C.F.[Cai-Feng],
Deep Salient Object Detection With Contextual Information Guidance,
IP(29), No. 1, 2020, pp. 360-374.
IEEE DOI 1910
convolutional neural nets, learning (artificial intelligence), object detection, deep salient object detection, multi-level contextual information integration BibRef

Liu, Y.[Yi], Zhang, Q.[Qiang], Zhang, D.W.[Ding-Wen], Han, J.G.[Jun-Gong],
Employing Deep Part-Object Relationships for Salient Object Detection,
ICCV19(1232-1241)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), object detection, convolutional neural networks, Noise measurement BibRef

Xu, Z.Z.[Zhao-Zhuo], Xu, X.[Xin], Wang, L.[Lei], Yang, R.[Rui], Pu, F.L.[Fang-Ling],
Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Guo, W.[Wei], Yang, W.[Wen], Zhang, H.J.[Hai-Jian], Hua, G.[Guang],
Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802
BibRef

Maninis, K.K.[Kevis-Kokitsi], Pont-Tuset, J.[Jordi], Arbeláez, P.[Pablo], Van Gool, L.J.[Luc J.],
Convolutional Oriented Boundaries: From Image Segmentation to High-Level Tasks,
PAMI(40), No. 4, April 2018, pp. 819-833.
IEEE DOI 1804
BibRef
Earlier:
Convolutional Oriented Boundaries,
ECCV16(I: 580-596).
Springer DOI 1611
computer vision, image classification, image representation, image segmentation, neural nets, object detection, COB, semantic contours BibRef

Shi, W.W.[Wei-Wei], Gong, Y.H.[Yi-Hong], Cheng, D.[De], Tao, X.Y.[Xiao-Yu], Zheng, N.N.[Nan-Ning],
Entropy and orthogonality based deep discriminative feature learning for object recognition,
PR(81), 2018, pp. 71-80.
Elsevier DOI 1806
Convolutional neural network (CNN), Discriminative feature learning, Entropy, Orthogonality, Object recognition BibRef

Bi, L.[Lei], Feng, D.[Dagan], Kim, J.M.[Jin-Man],
Dual-Path Adversarial Learning for Fully Convolutional Network (FCN)-Based Medical Image Segmentation,
VC(34), No. 6-8, June 2018, pp. 1043-1052.
WWW Link. 1806
BibRef

Ding, P.[Peng], Zhang, Y.[Ye], Deng, W.J.[Wei-Jian], Jia, P.[Ping], Kuijper, A.[Arjan],
A Light and Faster Regional Convolutional Neural Network for Object Detection in Optical Remote Sensing Images,
PandRS(141), 2018, pp. 208-218.
Elsevier DOI 1806
Deep convolution neural network, Deep learning (DL), Remote sensing images, Object detection BibRef

Zeng, X.Y.[Xing-Yu], Ouyang, W.L.[Wan-Li], Yan, J.J.[Jun-Jie], Li, H.S.[Hong-Sheng], Xiao, T.[Tong], Wang, K.[Kun], Liu, Y.[Yu], Zhou, Y.C.[Yu-Cong], Yang, B.[Bin], Wang, Z.[Zhe], Zhou, H.[Hui], Wang, X.G.[Xiao-Gang],
Crafting GBD-Net for Object Detection,
PAMI(40), No. 9, September 2018, pp. 2109-2123.
IEEE DOI 1808
gated bi-directional CNN. Object detection, Rabbits, Visualization, Feature extraction, Head, Proposals, Logic gates, Convolutional neural network, CNN, object detection 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

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

Liang, X.D.[Xiao-Dan], Lin, L.[Liang], Wei, Y.C.[Yun-Chao], Shen, X.H.[Xiao-Hui], Yang, J.C.[Jian-Chao], Yan, S.C.[Shui-Cheng],
Proposal-Free Network for Instance-Level Object Segmentation,
PAMI(40), No. 12, December 2018, pp. 2978-2991.
IEEE DOI 1811
Convolutional neural networks, Object segmentation, Semantics, Image segmentation, Object detection, Neural networks, convolutional neural network BibRef

Wang, W.G.[Wen-Guan], Zhao, S.Y.[Shu-Yang], Shen, J.B.[Jian-Bing], Hoi, S.C.H.[Steven C. H.], Borji, A.[Ali],
Deeply Supervised Salient Object Detection with Short Connections,
PAMI(41), No. 4, April 2019, pp. 815-828.
IEEE DOI 1903
BibRef
And:
Salient Object Detection With Pyramid Attention and Salient Edges,
CVPR19(1448-1457).
IEEE DOI 2002
BibRef
Earlier: CVPR17(5300-5309)
IEEE DOI 1711
Object detection, Feature extraction, Image edge detection, Image segmentation, Semantics, Saliency detection, edge detection. Computer architecture, Image edge detection, Neural networks. BibRef

Fan, D.P.[Deng-Ping], Zhai, Y.J.[Ying-Jie], Borji, A.[Ali], Yang, J.F.[Ju-Feng], Shao, L.[Ling],
BBS-net: RGB-d Salient Object Detection with a Bifurcated Backbone Strategy Network,
ECCV20(275-292).
Springer DOI 2010
BibRef

Zhu, J.H.[Ji-Hua], Wang, J.X.[Jia-Xing], Pang, S.M.[Shan-Min], Guan, W.[Weili], Li, Z.Y.[Zhong-Yu], Li, Y.C.[Yao-Chen], Qian, X.M.[Xue-Ming],
Co-weighting semantic convolutional features for object retrieval,
JVCIR(62), 2019, pp. 368-380.
Elsevier DOI 1908
Object retrieval, Deep convolutional features, Aggregation BibRef

Ghassemi, S.[Sina], Fiandrotti, A.[Attilio], Francini, G.[Gianluca], Magli, E.[Enrico],
Learning and Adapting Robust Features for Satellite Image Segmentation on Heterogeneous Data Sets,
GeoRS(57), No. 9, September 2019, pp. 6517-6529.
IEEE DOI 1909
Image segmentation, Satellites, Training, Semantics, Feature extraction, Labeling, Computer architecture, satellite image segmentation BibRef

Zhang, R.M.[Rui-Mao], Lin, L.[Liang], Wang, G.R.[Guang-Run], Wang, M.[Meng], Zuo, W.M.[Wang-Meng],
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions,
PAMI(41), No. 3, March 2019, pp. 596-610.
IEEE DOI 1902
Semantics, Labeling, Training, Neural networks, Task analysis, Predictive models, Image segmentation, Scene parsing, recursive structured prediction BibRef

Lin, L.[Liang], Wang, G.R.[Guang-Run], Zhang, R.[Rui], Zhang, R.M.[Rui-Mao], Liang, X.D.[Xiao-Dan], Zuo, W.M.[Wang-Meng],
Deep Structured Scene Parsing by Learning with Image Descriptions,
CVPR16(2276-2284)
IEEE DOI 1612
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

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
BibRef

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

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
BibRef

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

Arnab, A.[Anurag], Zheng, S.[Shuai], Jayasumana, S.[Sadeep], Romera-Paredes, B.[Bernardino], Larsson, M., Kirillov, A., Savchynskyy, B., Rother, C., Kahl, F., Torr, P.H.S.[Philip H. S.],
Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction,
SPMag(35), No. 1, January 2018, pp. 37-52.
IEEE DOI 1801
Computational modeling, Computer vision, Feature extraction, Image segmentation, Semantics, Visualization BibRef

Arnab, A.[Anurag], Jayasumana, S.[Sadeep], Zheng, S.[Shuai], Torr, P.H.S.[Philip H. S.],
Higher Order Conditional Random Fields in Deep Neural Networks,
ECCV16(II: 524-540).
Springer DOI 1611
BibRef

Arnab, A.[Anurag], Torr, P.H.S.[Philip H. S.],
Pixelwise Instance Segmentation with a Dynamically Instantiated Network,
CVPR17(879-888)
IEEE DOI 1711
BibRef
Earlier:
Bottom-up Instance Segmentation using Deep Higher-Order CRFs,
BMVC16(xx-yy).
HTML Version. 1805
Detectors, Image segmentation, Object detection, Pipelines, Proposals, Semantics. First, semantic segmentation, then object instance detection. BibRef

Zheng, S.[Shuai], Jayasumana, S.[Sadeep], Romera-Paredes, B.[Bernardino], Vineet, V.[Vibhav], Su, Z.Z.[Zhi-Zhong], Du, D.L.[Da-Long], Huang, C.[Chang], Torr, P.H.S.[Philip H. S.],
Conditional Random Fields as Recurrent Neural Networks,
ICCV15(1529-1537)
IEEE DOI 1602
Combine CNN with CRF. BibRef

Larsson, M.[Måns], Alvén, J.[Jennifer], Kahl, F.[Fredrik],
Max-Margin Learning of Deep Structured Models for Semantic Segmentation,
SCIA17(II: 28-40).
Springer DOI 1706
BibRef

Saleh, F.S.[Fatemehsadat S.], Aliakbarian, M.S.[Mohammad Sadegh], Salzmann, M.[Mathieu], Petersson, L.[Lars], Alvarez, J.M.[Jose M.], Gould, S.[Stephen],
Incorporating Network Built-in Priors in Weakly-Supervised Semantic Segmentation,
PAMI(40), No. 6, June 2018, pp. 1382-1396.
IEEE DOI 1805
BibRef
Earlier: A1, A2, A3, A4, A6, A5:
Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation,
ECCV16(VIII: 413-432).
Springer DOI 1611
Data mining, Image segmentation, Machine learning, Neural networks, Object recognition, Semantics, Training, Semantic segmentation, weakly-supervised semantic segmentation BibRef

Saleh, F.S.[Fatemehsadat S.], Aliakbarian, M.S.[Mohammad Sadegh], Salzmann, M.[Mathieu], Petersson, L.[Lars], Alvarez, J.M.[Jose M.],
Bringing Background into the Foreground: Making All Classes Equal in Weakly-Supervised Video Semantic Segmentation,
ICCV17(2125-2135)
IEEE DOI 1802
image classification, image segmentation, learning (artificial intelligence), video signal processing, Semantics BibRef

Lin, G., Shen, C., van den Hengel, A.J.[Anton J.], Reid, I.D.,
Exploring Context with Deep Structured Models for Semantic Segmentation,
PAMI(40), No. 6, June 2018, pp. 1352-1366.
IEEE DOI 1805
BibRef
Earlier:
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation,
CVPR16(3194-3203)
IEEE DOI 1612
Context, Context modeling, Image resolution, Image segmentation, Neural networks, Semantics, Training, Semantic segmentation, convolutional neural networks BibRef

Lin, G.S.[Guo-Sheng], Liu, F.[Fayao], Milan, A.[Anton], Shen, C.H.[Chun-Hua], Reid, I.D.[Ian D.],
RefineNet: Multi-Path Refinement Networks for Dense Prediction,
PAMI(42), No. 5, May 2020, pp. 1228-1242.
IEEE DOI 2004
BibRef
Earlier: A1, A3, A4, A5, Only:
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation,
CVPR17(5168-5177)
IEEE DOI 1711
Semantics, Estimation, Image segmentation, Task analysis, Convolution, Training, Visualization, Convolutional neural network, dense prediction. Computer architecture, Image resolution, Image segmentation, Semantics, Training BibRef

Volpi, M.[Michele], Tuia, D.[Devis],
Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images,
PandRS(144), 2018, pp. 48-60.
Elsevier DOI 1809
Semantic segmentation, Semantic boundary detection, Convolutional neural networks, Conditional random fields, Aerial imagery BibRef

Chen, B., Gong, C., Yang, J.,
Importance-Aware Semantic Segmentation for Autonomous Vehicles,
ITS(20), No. 1, January 2019, pp. 137-148.
IEEE DOI 1901
Image segmentation, Autonomous vehicles, Roads, Neural networks, Feature extraction, Semantics, Reliability, Semantic segmentation, autonomous driving BibRef

Fu, K.[Kun], Lu, W.X.[Wan-Xuan], Diao, W.H.[Wen-Hui], Yan, M.L.[Meng-Long], Sun, H.[Hao], Zhang, Y.[Yi], Sun, X.[Xian],
WSF-NET: Weakly Supervised Feature-Fusion Network for Binary Segmentation in Remote Sensing Image,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Nguyen, T.V., Nguyen, K., Do, T.,
Semantic Prior Analysis for Salient Object Detection,
IP(28), No. 6, June 2019, pp. 3130-3141.
IEEE DOI 1905
Semantics, Object detection, Image color analysis, Deep learning, Saliency detection, Task analysis, Visualization, deep networks BibRef

Redondo-Cabrera, C., Baptista-Ríos, M., López-Sastre, R.J.,
Learning to Exploit the Prior Network Knowledge for Weakly Supervised Semantic Segmentation,
IP(28), No. 7, July 2019, pp. 3649-3661.
IEEE DOI 1906
Image segmentation, Semantics, Training, Task analysis, Data models, Training data, Tools, Semantic segmentation, weakly supervised, deep learning BibRef

Guo, D., Pei, Y., Zheng, K., Yu, H., Lu, Y., Wang, S.,
Degraded Image Semantic Segmentation With Dense-Gram Networks,
IP(29), No. 1, 2020, pp. 782-795.
IEEE DOI 1910
Image segmentation, Semantics, Degradation, Training, Motion segmentation, Image restoration, Image texture, degraded images BibRef

Audebert, N.[Nicolas], Boulch, A.[Alexandre], Le Saux, B.[Bertrand], Lefèvre, S.[Sébastien],
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Elsevier DOI 1911
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IEEE DOI 1709
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Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks,
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Springer DOI 1704
Buildings, Labeling, Optical imaging, Roads, Semantics, Sensors, Training BibRef

Zhao, W., Hou, X., Yu, X., He, Y., Lu, H.,
Towards Weakly-Supervised Focus Region Detection via Recurrent Constraint Network,
IP(29), No. , 2020, pp. 1356-1367.
IEEE DOI 1911
Training, Task analysis, Object segmentation, Semantics, Image segmentation, Dogs, Focus region detection, box-level supervision BibRef

Zhang, T., Lin, G., Cai, J., Shen, T., Shen, C., Kot, A.C.,
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation,
MultMed(21), No. 11, November 2019, pp. 2930-2941.
IEEE DOI 1911
Image segmentation, Semantics, Detectors, Training, Task analysis, Pipelines, Object recognition, Semantic segmentation, weakly-supervised learning BibRef

Mi, L.[Li], Chen, Z.Z.[Zhen-Zhong],
Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation,
PandRS(159), 2020, pp. 140-152.
Elsevier DOI 1912
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Elsevier DOI 2009
Neural forest, Superpixel, Remote sensing imagery, Semantic segmentation BibRef

Ding, H., Jiang, X., Shuai, B., Liu, A.Q., Wang, G.,
Semantic Segmentation With Context Encoding and Multi-Path Decoding,
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IEEE DOI 2002
Semantic segmentation, context encoding, gated sum, boundary delineation refinement, deep learning, CGBNet, convolutional neural networks BibRef

Wang, Y.D.[Yin-Duo], Zhang, H.F.[Hao-Feng], Wang, S.D.[Shi-Dong], Long, Y.[Yang], Yang, L.[Longzhi],
Semantic combined network for zero-shot scene parsing,
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DOI Link 2003
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Zhang, Y.[Yang], David, P.[Philip], Foroosh, H.[Hassan], Gong, B.[Boqing],
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IEEE DOI 2007
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Earlier: A1, A2, A4, Only:
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes,
ICCV17(2039-2049)
IEEE DOI 1802
Semantics, Image segmentation, Task analysis, Adaptation models, Neural networks, Training, Buildings, Domain adaptation, self-driving. computer graphics, convolution, image classification, learning (artificial intelligence). BibRef

Peng, C.L.[Cheng-Li], Ma, J.Y.[Jia-Yi],
Semantic segmentation using stride spatial pyramid pooling and dual attention decoder,
PR(107), 2020, pp. 107498.
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Semantic segmentation, Convolutional neural networks, Pyramid pooling, Attention mechanism BibRef

Zhang, X., Wei, Y., Yang, Y., Huang, T.S.,
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation,
Cyber(50), No. 9, September 2020, pp. 3855-3865.
IEEE DOI 2008
Image segmentation, Feature extraction, Testing, Semantics, Training, Task analysis, Dogs, Few-shot learning, image segmentation, siamese network BibRef

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

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,
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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

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-Viñolo, 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

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

Diakogiannis, F.I.[Foivos I.], Waldner, F.[François], Caccetta, P.[Peter], Wu, C.[Chen],
ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data,
PandRS(162), 2020, pp. 94-114.
Elsevier DOI 2004
Convolutional neural network, Loss function, Architecture, Data augmentation, Very high spatial resolution BibRef

Qiu, S., Zhao, Y., Jiao, J., Wei, Y., Wei, S.,
Referring Image Segmentation by Generative Adversarial Learning,
MultMed(22), No. 5, May 2020, pp. 1333-1344.
IEEE DOI 2005
Image segmentation, Semantics, Feature extraction, Natural languages, Generators, Generative adversarial networks, Adversarial training BibRef

Chen, X., Lou, X., Bai, L., Han, J.,
Residual Pyramid Learning for Single-Shot Semantic Segmentation,
ITS(21), No. 7, July 2020, pp. 2990-3000.
IEEE DOI 2007
Semantics, Feature extraction, Task analysis, Decoding, Training, Image segmentation, Neural networks, Intelligent vehicles, residual learning BibRef

Ren, X., Ahmad, S., Zhang, L., Xiang, L., Nie, D., Yang, F., Wang, Q., Shen, D.,
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation,
IP(29), 2020, pp. 7497-7510.
IEEE DOI 2007
Semantic segmentation, fully convolutional network, task decomposition, sync-regularization, deep learning BibRef

Feng, M.[Mingtao], Zhang, L.[Liang], Lin, X.[Xuefei], Gilani, S.Z.[Syed Zulqarnain], Mian, A.[Ajmal],
Point attention network for semantic segmentation of 3D point clouds,
PR(107), 2020, pp. 107446.
Elsevier DOI 2008
Semantic segmentation, 3D point cloud, Point attention network, Deep learning BibRef

Kim, W., Kanezaki, A., Tanaka, M.,
Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering,
IP(29), 2020, pp. 8055-8068.
IEEE DOI 2008
Image segmentation, Training, Feature extraction, Semantics, Machine learning, Clustering algorithms, Unsupervised learning, feature clustering BibRef

Wang, Y.[Yidong], Mo, L.[Lisha], Ma, H.[Huimin], Yuan, J.[Jian],
OccGAN: Semantic image augmentation for driving scenes,
PRL(136), 2020, pp. 257-263.
Elsevier DOI 2008
Occlusion, GAN, Semantic, Augmentation, Cityscapes BibRef

Di Mauro, D.[Daniele], Furnari, A.[Antonino], Patanè, G.[Giuseppe], Battiato, S.[Sebastiano], Farinella, G.M.[Giovanni Maria],
SceneAdapt: Scene-based domain adaptation for semantic segmentation using adversarial learning,
PRL(136), 2020, pp. 175-182.
Elsevier DOI 2008
Semantic segmentation, Domain adaptation, Scene adaptation, Adversarial learning, BibRef

Cermelli, F., Mancini, M., Rota Bulò, S., Ricci, E., Caputo, B.,
Modeling the Background for Incremental Learning in Semantic Segmentation,
CVPR20(9230-9239)
IEEE DOI 2008
Semantics, Image segmentation, Task analysis, Standards, Training, Context modeling, Computer architecture BibRef

Lai, H.J.[Han-Jiang], Chen, J.K.[Ji-Kai], Geng, L.B.[Li-Bing], Pan, Y.[Yan], Liang, X.D.[Xiao-Dan], Yin, J.[Jian],
Improving Deep Binary Embedding Networks by Order-Aware Reweighting of Triplets,
CirSysVideo(30), No. 4, April 2020, pp. 1162-1172.
IEEE DOI 2004
Binary codes, Training, Hash functions, Image retrieval, Semantics, Quantization (signal), Dogs, Image retrieval, triplet ranking loss, nearest neighbor search BibRef

Xu, Y., Dai, W., Qi, Y., Zou, J., Xiong, H.,
Iterative Deep Neural Network Quantization With Lipschitz Constraint,
MultMed(22), No. 7, July 2020, pp. 1874-1888.
IEEE DOI 2007
Quantization (signal), Neural networks, Convolution, Computational modeling, Semantics, Object detection, Image coding, Lipschitz constraint BibRef

Tasar, O., Happy, S.L., Tarabalka, Y., Alliez, P.,
ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks,
GeoRS(58), No. 10, October 2020, pp. 7178-7193.
IEEE DOI 2009
Training, Remote sensing, Image segmentation, Training data, Semantics, Image color analysis, Generative adversarial networks, semantic segmentation BibRef

Lv, F.M.[Feng-Mao], Liu, H.Y.[Hai-Yang], Wang, Y.C.[Yi-Chen], Zhao, J.Y.[Jia-Yi], Yang, G.W.[Guo-Wu],
Learning Unbiased Zero-Shot Semantic Segmentation Networks Via Transductive Transfer,
SPLetters(27), 2020, pp. 1640-1644.
IEEE DOI 2010
Semantics, Image segmentation, Neural networks, Visualization, Training, Predictive models, Machine learning, zero-shot learning BibRef

Cao, J., Pang, Y., Zhao, S., Li, X.,
High-Level Semantic Networks for Multi-Scale Object Detection,
CirSysVideo(30), No. 10, October 2020, pp. 3372-3386.
IEEE DOI 2010
Semantics, Feature extraction, Object detection, Proposals, Face detection, Convolution, Face, Object detection, receptive field BibRef

Guo, R.X.[Rong-Xin], Sun, X.[Xian], Chen, K.Q.[Kai-Qiang], Zhou, X.[Xiao], Yan, Z.Y.[Zhi-Yuan], Diao, W.H.[Wen-Hui], Yan, M.L.[Meng-Long],
JMLNet: Joint Multi-Label Learning Network for Weakly Supervised Semantic Segmentation in Aerial Images,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
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Baheti, B.[Bhakti], Innani, S.[Shubham], Gajre, S.[Suhas], Talbar, S.[Sanjay],
Semantic scene segmentation in unstructured environment with modified DeepLabV3+,
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Elsevier DOI 2010
Semantic Segmentation, Convolutional Neural Network(CNN), Xception, MobileNetV2 BibRef


Isaacs, O.[Or], Shayer, O.[Oran], Lindenbaum, M.[Michael],
Enhancing Generic Segmentation With Learned Region Representations,
CVPR20(12943-12952)
IEEE DOI 2008
Image segmentation, Task analysis, Image edge detection, Training, Semantics, Face, Image color analysis BibRef

Ouali, Y., Hudelot, C., Tami, M.,
Semi-Supervised Semantic Segmentation With Cross-Consistency Training,
CVPR20(12671-12681)
IEEE DOI 2008
Decoding, Training, Perturbation methods, Semantics, Image segmentation, Predictive models, Task analysis BibRef

Ibrahim, M.S., Vahdat, A., Ranjbar, M., Macready, W.G.,
Semi-Supervised Semantic Image Segmentation With Self-Correcting Networks,
CVPR20(12712-12722)
IEEE DOI 2008
Image segmentation, Training, Semantics, Predictive models, Decoding, Noise measurement, Robustness BibRef

Kim, M., Byun, H.,
Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation,
CVPR20(12972-12981)
IEEE DOI 2008
Adaptation models, Image segmentation, Semantics, Training, Visualization, Predictive models, Task analysis BibRef

Zhong, Z., Lin, Z.Q., Bidart, R., Hu, X., Daya, I.B., Li, Z., Zheng, W., Li, J., Wong, A.,
Squeeze-and-Attention Networks for Semantic Segmentation,
CVPR20(13062-13071)
IEEE DOI 2008
Image segmentation, Semantics, Convolution, Feature extraction, Task analysis, Head, Kernel BibRef

Li, X., Yang, Y., Zhao, Q., Shen, T., Lin, Z., Liu, H.,
Spatial Pyramid Based Graph Reasoning for Semantic Segmentation,
CVPR20(8947-8956)
IEEE DOI 2008
Cognition, Convolution, Laplace equations, Semantics, Task analysis, Symmetric matrices, Computer vision BibRef

Tasar, O., Tarabalka, Y., Giros, A., Alliez, P., Clerc, S.,
StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization,
EarthVision20(747-756)
IEEE DOI 2008
Urban areas, Generators, Remote sensing, Task analysis, Semantics, Image segmentation BibRef

Klingner, M., Bär, A., Fingscheidt, T.,
Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation,
SAIAD20(1299-1309)
IEEE DOI 2008
Semantics, Robustness, Training, Estimation, Image segmentation, Task analysis, Perturbation methods BibRef

Wang, Z., Yu, M., Wei, Y., Feris, R., Xiong, J., Hwu, W., Huang, T.S., Shi, H.,
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation,
CVPR20(12632-12641)
IEEE DOI 2008
Feature extraction, Semantics, Task analysis, Training, Image segmentation, Adaptation models, Generators BibRef

Iqbal, J., Ali, M.,
MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling,
WACV20(1853-1862)
IEEE DOI 2006
Semantics, Image segmentation, Adaptation models, Training, Computational modeling, Task analysis, Roads BibRef

Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.,
PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment,
ICCV19(9196-9205)
IEEE DOI 2004
convolutional neural nets, image representation, image segmentation, learning (artificial intelligence), PANet, Silicon BibRef

Li, X., Zhong, Z., Wu, J., Yang, Y., Lin, Z., Liu, H.,
Expectation-Maximization Attention Networks for Semantic Segmentation,
ICCV19(9166-9175)
IEEE DOI 2004
computer vision, expectation-maximisation algorithm, feature extraction, image representation, image segmentation, Convergence BibRef

Zou, Y., Yu, Z., Liu, X., Kumar, B.V.K.V., Wang, J.,
Confidence Regularized Self-Training,
ICCV19(5981-5990)
IEEE DOI 2004
Code, Segmentation.
WWW Link. image classification, image segmentation, iterative methods, unsupervised learning, pseudolabels, overconfident label belief, Semantics BibRef

Fu, J., Liu, J., Wang, Y., Li, Y., Bao, Y., Tang, J., Lu, H.,
Adaptive Context Network for Scene Parsing,
ICCV19(6747-6756)
IEEE DOI 2004
feature extraction, image segmentation, neural nets, context module, adaptive contextual features, Semantics BibRef

Lv, F.M.[Feng-Mao], Liang, T.[Tao], Chen, X.[Xiang], Lin, G.S.[Guo-Sheng],
Cross-Domain Semantic Segmentation via Domain-Invariant Interactive Relation Transfer,
CVPR20(4333-4342)
IEEE DOI 2008
Image segmentation, Semantics, Adaptation models, Training, Image reconstruction, Neural networks, Biological system modeling BibRef

Lian, Q.[Qing], Duan, L.X.[Li-Xin], Lv, F.M.[Feng-Mao], Gong, B.Q.[Bo-Qing],
Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach,
ICCV19(6757-6766)
IEEE DOI 2004
generalisation (artificial intelligence), image segmentation, neural nets, unsupervised learning, generalization capability, Logistics BibRef

Zhang, F., Chen, Y., Li, Z., Hong, Z., Liu, J., Ma, F., Han, J., Ding, E.,
ACFNet: Attentional Class Feature Network for Semantic Segmentation,
ICCV19(6797-6806)
IEEE DOI 2004
image representation, image segmentation, ACFNet, semantic segmentation, spatial perspective, Training BibRef

Lee, J., Kim, E., Lee, S., Lee, J., Yoon, S.,
Frame-to-Frame Aggregation of Active Regions in Web Videos for Weakly Supervised Semantic Segmentation,
ICCV19(6807-6817)
IEEE DOI 2004
image representation, image segmentation, image sequences, Internet, neural nets, object detection, supervised learning, Object recognition BibRef

Choi, J., Kim, T., Kim, C.,
Self-Ensembling With GAN-Based Data Augmentation for Domain Adaptation in Semantic Segmentation,
ICCV19(6829-6839)
IEEE DOI 2004
image classification, image segmentation, neural nets, unsupervised learning, GAN-based data augmentation, Feature extraction BibRef

Zhong, C., Hu, Z., Li, M., Li, H., Yang, X., Liu, F.,
Dual Stream Segmentation Network for Real-Time Semantic Segmentation,
ICIVC20(144-149)
IEEE DOI 2009
Semantics, Real-time systems, Streaming media, Image segmentation, Spatial resolution, Computer architecture, Feature extraction, TwoBranch Framework BibRef

Li, Y., Song, L., Chen, Y., Li, Z., Zhang, X., Wang, X., Sun, J.,
Learning Dynamic Routing for Semantic Segmentation,
CVPR20(8550-8559)
IEEE DOI 2008
Routing, Semantics, Computer architecture, Network architecture, Logic gates, Dynamic scheduling, Computer vision BibRef

Liu, Q., Kampffmeyer, M., Jenssen, R., Salberg, A.,
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation,
AgriVision20(199-205)
IEEE DOI 2008
Semantics, Training, Computer architecture, Task analysis, Atmospheric modeling, Adaptation models BibRef

Bar, A., Klingner, M., Varghese, S., Hüger, F., Schlicht, P., Fingscheidt, T.,
Robust Semantic Segmentation by Redundant Networks With a Layer-Specific Loss Contribution and Majority Vote,
SAIAD20(1348-1358)
IEEE DOI 2008
Robustness, Semantics, Task analysis, Adaptive systems, Image segmentation, Training, Neural networks BibRef

Pavlitskaya, S., Hubschneider, C., Weber, M., Moritz, R., Hüger, F., Schlicht, P., Zöllner, J.M.,
Using Mixture of Expert Models to Gain Insights into Semantic Segmentation,
SAIAD20(1399-1406)
IEEE DOI 2008
Logic gates, Computer architecture, Feature extraction, Uncertainty, Semantics, Neural networks, Task analysis BibRef

Nekrasov, V., Shen, C., Reid, I.D.,
Template-Based Automatic Search of Compact Semantic Segmentation Architectures,
WACV20(1969-1978)
IEEE DOI 2006
Computer architecture, Image segmentation, Semantics, Task analysis, Convolution, Recurrent neural networks, Benchmark testing BibRef

Yuan, J., Deng, Z., Wang, S., Luo, Z.,
Multi Receptive Field Network for Semantic Segmentation,
WACV20(1883-1892)
IEEE DOI 2006
Image edge detection, Semantics, Task analysis, Image segmentation, Training, Feature extraction, Standards BibRef

Zeng, Y., Zhang, P., Lin, Z., Zhang, J., Lu, H.,
Towards High-Resolution Salient Object Detection,
ICCV19(7233-7242)
IEEE DOI 2004
image resolution, image segmentation, neural nets, object detection, semantic networks, low resolutions, Image resolution BibRef

Zhang, C., Lin, G., Liu, F., Guo, J., Wu, Q., Yao, R.,
Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation,
ICCV19(9586-9594)
IEEE DOI 2004
data structures, graph theory, image representation, image segmentation, message passing, data representations, BibRef

Xu, Y.[Yajun], Mao, Z.[Zhendong], Zhang, P.[Peng], Wang, B.[Bin],
Compact Position-aware Attention Network for Image Semantic Segmentation,
MMMod20(II:639-650).
Springer DOI 2003
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Jain, S.[Samvit], Wang, X.[Xin], Gonzalez, J.E.[Joseph E.],
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video,
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IEEE DOI 2002
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Fu, J.[Jun], Liu, J.[Jing], Tian, H.[Haijie], Li, Y.[Yong], Bao, Y.J.[Yong-Jun], Fang, Z.W.[Zhi-Wei], Lu, H.Q.[Han-Qing],
Dual Attention Network for Scene Segmentation,
CVPR19(3141-3149).
IEEE DOI 2002
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Zhuang, B.[Bohan], Shen, C.H.[Chun-Hua], Tan, M.[Mingkui], Liu, L.Q.[Ling-Qiao], Reid, I.D.[Ian D.],
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation,
CVPR19(413-422).
IEEE DOI 2002
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Wang, K., Wang, C., Tai, T., Wang, J.,
Object Bounding Transformed Network for End-to-End Semantic Segmentation,
ICIP19(3217-3221)
IEEE DOI 1910
image semantic segmentation, Object Boundary Guide, Doman Transform Network, ResNet 101 BibRef

Pham, Q., Hua, B., Nguyen, T., Yeung, S.,
Real-Time Progressive 3D Semantic Segmentation for Indoor Scenes,
WACV19(1089-1098)
IEEE DOI 1904
image reconstruction, image segmentation, time progressive 3D, widespread adoption, autonomous systems, assistant robots, Neural networks BibRef

Takikawa, T., Acuna, D., Jampani, V., Fidler, S.,
Gated-SCNN: Gated Shape CNNs for Semantic Segmentation,
ICCV19(5228-5237)
IEEE DOI 2004
convolutional neural nets, image representation, image segmentation, object detection, image segmentation form, Task analysis BibRef

Wu, Z., Wang, X., Gonzalez, J., Goldstein, T., Davis, L.S.,
ACE: Adapting to Changing Environments for Semantic Segmentation,
ICCV19(2121-2130)
IEEE DOI 2004
gradient methods, image segmentation, learning (artificial intelligence), neural nets, Lighting BibRef

Du, L., Tan, J., Yang, H., Feng, J., Xue, X., Zheng, Q., Ye, X., Zhang, X.,
SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network for Semantic Segmentation,
ICCV19(982-991)
IEEE DOI 2004
feature extraction, image segmentation, learning (artificial intelligence), semantic segmentation, Data models BibRef

Zhu, Z., Xu, M., Bai, S., Huang, T., Bai, X.,
Asymmetric Non-Local Neural Networks for Semantic Segmentation,
ICCV19(593-602)
IEEE DOI 2004
Code, Segmentation.
WWW Link. image fusion, image segmentation, neural nets, asymmetric nonlocal neural networks, Semantics BibRef

Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.,
CCNet: Criss-Cross Attention for Semantic Segmentation,
ICCV19(603-612)
IEEE DOI 2004
Code, Segmentation.
WWW Link. computer vision, image segmentation, information retrieval, learning (artificial intelligence), recurrent neural nets, Complexity theory BibRef

Samson, L., van Noord, N., Booij, O., Hofmann, M., Gavves, E., Ghafoorian, M.,
I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation,
CVRSUAD19(951-960)
IEEE DOI 2004
image segmentation, learning (artificial intelligence), neural nets, object detection, pixel-wise metrics, Semantic segmentation BibRef

Kato, N., Yamasaki, T., Aizawa, K.,
Zero-Shot Semantic Segmentation via Variational Mapping,
MDALC19(1363-1370)
IEEE DOI 2004
image segmentation, learning (artificial intelligence), neural nets, object detection, object recognition, semantic segmentation BibRef

Zhu, L., Wang, T., Aksu, E., Kamarainen, J.,
Cross-Granularity Attention Network for Semantic Segmentation,
NeruArch19(1920-1930)
IEEE DOI 2004
convolutional neural nets, feature extraction, image segmentation, neural net architecture, object detection, neural architecture BibRef

Shaw, A., Hunter, D., Landola, F., Sidhu, S.,
SqueezeNAS: Fast Neural Architecture Search for Faster Semantic Segmentation,
NeruArch19(2014-2024)
IEEE DOI 2004
image classification, image segmentation, neural net architecture, optimisation, parallel processing, Deep Learning BibRef

Bahl, G., Daniel, L., Moretti, M., Lafarge, F.,
Low-Power Neural Networks for Semantic Segmentation of Satellite Images,
LPCV19(2469-2476)
IEEE DOI 2004
convolutional neural nets, field programmable gate arrays, geophysical image processing, image coding, Cloud BibRef

Michieli, U., Zanuttigh, P.,
Incremental Learning Techniques for Semantic Segmentation,
TASKCV19(3205-3212)
IEEE DOI 2004
computer vision, feature extraction, image classification, image segmentation, learning (artificial intelligence), Catastrophic Forgetting BibRef

Zhuang, J., Yang, J., Gu, L., Dvornek, N.,
ShelfNet for Fast Semantic Segmentation,
CVRSUAD19(847-856)
IEEE DOI 2004
Code, Segmentation.
WWW Link. image segmentation, image understanding, semantic segmentation, PASCAL VOC dataset, PSPNet, ResNet34 backbone, ShelfNet, Realtime 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.M.[Sung-Min], 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.M.[Nuno M.],
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.G.[Xin-Ge], 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

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

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

Karim, R.[Rezaul], Islam, M.A.[M. Amirul], Bruce, N.D.B.[Neil D. B.],
Distributed Iterative Gating Networks for Semantic Segmentation,
WACV20(2833-2842)
IEEE DOI 2006
BibRef
Earlier:
Recurrent Iterative Gating Networks for Semantic Segmentation,
WACV19(1070-1079)
IEEE DOI 1904
Semantics, Logic gates, Feeds, Modulation, Spatial resolution, Labeling, Signal resolution. image segmentation, iterative methods, learning (artificial intelligence), neural net architecture. BibRef

Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., Schroers, C.,
Normalized Cut Loss for Weakly-Supervised CNN Segmentation,
CVPR18(1818-1827)
IEEE DOI 1812
Image segmentation, Training, Proposals, Standards, Semisupervised learning, Entropy, Semantics BibRef

Zhuang, Y., Tao, L., Yang, F., Ma, C., Zhang, Z., Jia, H., Xie, X.,
RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation,
ICPR18(1506-1511)
IEEE DOI 1812
Feature extraction, Convolution, Image segmentation, Training, Estimation, Semantics, Correlation BibRef

Zhuang, Y., Yang, F., Tao, L., Ma, C., Zhang, Z., Li, Y., Jia, H., Xie, X., Gao, W.,
Dense Relation Network: Learning Consistent and Context-Aware Representation for Semantic Image Segmentation,
ICIP18(3698-3702)
IEEE DOI 1809
Feature extraction, Semantics, Image segmentation, Training, Recurrent neural networks, Aggregates, Benchmark testing, Context-Restricted Loss BibRef

Yamashita, T., Furukawa, H., Fujiyoshi, H.,
Multiple Skip Connections of Dilated Convolution Network for Semantic Segmentation,
ICIP18(1593-1597)
IEEE DOI 1809
Convolution, Decoding, Semantics, Image segmentation, Task analysis, Deconvolution, Cameras, deep learning, semantic segmentation BibRef

Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., Cottrell, G.,
Understanding Convolution for Semantic Segmentation,
WACV18(1451-1460)
IEEE DOI 1806
convolution, feedforward neural nets, image coding, image resolution, image segmentation, Training BibRef

Ye, L., Liu, Z., Wang, Y.,
Learning Semantic Segmentation with Diverse Supervision,
WACV18(1461-1469)
IEEE DOI 1806
computer vision, feedforward neural nets, image classification, image segmentation, learning (artificial intelligence), Training 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

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

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

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

Zhong, M., Zeng, G.,
Efficient Object Region Discovery for Weakly-supervised Semantic Segmentation,
ICPR18(2166-2171)
IEEE DOI 1812
Image segmentation, Training, Semantics, Benchmark testing, Convolutional neural networks, Task analysis, Standards BibRef

Liang, X.D.[Xiao-Dan], Xing, E.[Eric], Zhou, H.F.[Hong-Fei],
Dynamic-Structured Semantic Propagation Network,
CVPR18(752-761)
IEEE DOI 1812
Semantics, Neurons, Task analysis, Image segmentation, Vocabulary, Training, Correlation BibRef

Yu, C.Q.[Chang-Qian], Wang, J.B.[Jing-Bo], Peng, C.[Chao], Gao, C.X.[Chang-Xin], Yu, G.[Gang], Sang, N.[Nong],
BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation,
ECCV18(XIII: 334-349).
Springer DOI 1810
BibRef

Wilhelm, T., Grzeszick, R., Fink, G.A., Woehler, C.,
From Weakly Supervised Object Localization to Semantic Segmentation by Probabilistic Image Modeling,
DICTA17(1-7)
IEEE DOI 1804
image segmentation, learning (artificial intelligence), object detection, convolutional network, deep learning, Training BibRef

Peng, C., Zhang, X., Yu, G., Luo, G., Sun, J.,
Large Kernel Matters: Improve Semantic Segmentation by Global Convolutional Network,
CVPR17(1743-1751)
IEEE DOI 1711
Computational modeling, Feature extraction, Image segmentation, Kernel, Semantics, Standards BibRef

Pohlen, T., Hermans, A.[Alexander], Mathias, M., Leibe, B.[Bastian],
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes,
CVPR17(3309-3318)
IEEE DOI 1711
Computer architecture, Image segmentation, Network architecture, Semantics, Streaming media, Training 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

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

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

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

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

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

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

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

Hong, S., Oh, J., Lee, H., Han, B.,
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network,
CVPR16(3204-3212)
IEEE DOI 1612
BibRef

Qi, G.J.[Guo-Jun],
Hierarchically Gated Deep Networks for Semantic Segmentation,
CVPR16(2267-2275)
IEEE DOI 1612
BibRef

Ran, L.Y.[Ling-Yan], Zhang, Y.N.[Yan-Ning], Hua, G.[Gang],
CANNET: Context aware nonlocal convolutional networks for semantic image segmentation,
ICIP15(4669-4673)
IEEE DOI 1512
Semantic segmentation; context aware module; sparse kernel BibRef

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


Last update:Oct 19, 2020 at 15:02:28