8.3.4.3.2 Convolutional Neural Networks for Semantic Segmentation, CNN

Chapter Contents (Back)
Neural Networks. Convolutional Neural Networks. CNN. Semantic Segmentation.
See also Convolutional Neural Networks for Object Detection and Segmentation.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Ding, H., Jiang, X., Shuai, B., Liu, A.Q., Wang, G.,
Semantic Segmentation With Context Encoding and Multi-Path Decoding,
IP(29), 2020, pp. 3520-3533.
IEEE DOI 2002
Semantic segmentation, context encoding, gated sum, boundary delineation refinement, deep learning, CGBNet, convolutional neural networks 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.
Elsevier DOI 2008
Semantic segmentation, Convolutional neural networks, Pyramid pooling, Attention mechanism 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
BibRef

Zhou, L.[Lei], Kong, X.Y.[Xiang-Yong], Gong, C.[Chen], Zhang, F.[Fan], Zhang, X.G.[Xiao-Guo],
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

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

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

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

Baheti, B.[Bhakti], Innani, S.[Shubham], Gajre, S.[Suhas], Talbar, S.[Sanjay],
Semantic scene segmentation in unstructured environment with modified DeepLabV3+,
PRL(138), 2020, pp. 223-229.
Elsevier DOI 2010
Semantic Segmentation, Convolutional Neural Network(CNN), Xception, MobileNetV2 BibRef

Mou, L.C.[Li-Chao], Hua, Y.S.[Yuan-Sheng], Zhu, X.X.[Xiao Xiang],
Relation Matters: Relational Context-Aware Fully Convolutional Network for Semantic Segmentation of High-Resolution Aerial Images,
GeoRS(58), No. 11, November 2020, pp. 7557-7569.
IEEE DOI 2011
BibRef
Earlier:
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes,
CVPR19(12408-12417).
IEEE DOI 2002
Semantics, Image segmentation, Task analysis, Visualization, Remote sensing, Cognition, Convolution, semantic segmentation BibRef

Sang, H.[Haiwei], Zhou, Q.[Qiuhao], Zhao, Y.[Yong],
PCANet: Pyramid convolutional attention network for semantic segmentation,
IVC(103), 2020, pp. 103997.
Elsevier DOI 2011
Non-local module, Atrous convolution, Attention mechanism, Semantic segmentation BibRef

Meng, F., Luo, K., Li, H., Wu, Q., Xu, X.,
Weakly Supervised Semantic Segmentation by a Class-Level Multiple Group Cosegmentation and Foreground Fusion Strategy,
CirSysVideo(30), No. 12, December 2020, pp. 4823-4836.
IEEE DOI 2012
Image segmentation, Semantics, Task analysis, Training, Videos, Convolutional neural networks, Integrated circuit modeling, region fusion BibRef

Li, Y., Li, X., Xiao, C., Li, H., Zhang, W.,
EACNet: Enhanced Asymmetric Convolution for Real-Time Semantic Segmentation,
SPLetters(28), 2021, pp. 234-238.
IEEE DOI 2102
Convolution, Semantics, Feature extraction, Training, Real-time systems, Standards, Image segmentation, depth-wise asymmetric convolution BibRef

Geng, Q., Zhang, H., Qi, X., Huang, G., Yang, R., Zhou, Z.,
Gated Path Selection Network for Semantic Segmentation,
IP(30), 2021, pp. 2436-2449.
IEEE DOI 2102
Semantics, Image segmentation, Convolution, Global Positioning System, Aggregates, Logic gates, adaptive receptive fields and sampling locations BibRef

Han, H.Y., Chen, Y.C., Hsiao, P.Y., Fu, L.C.,
Using Channel-Wise Attention for Deep CNN Based Real-Time Semantic Segmentation With Class-Aware Edge Information,
ITS(22), No. 2, February 2021, pp. 1041-1051.
IEEE DOI 2102
Semantics, Image segmentation, Task analysis, Real-time systems, Computer architecture, Roads, Convolution, Deep learning, edge information BibRef

Zhou, L., Gong, C., Liu, Z., Fu, K.,
SAL: Selection and Attention Losses for Weakly Supervised Semantic Segmentation,
MultMed(23), 2021, pp. 1035-1048.
IEEE DOI 2103
Annotations, Image segmentation, Semantics, Noise measurement, Training, Boundary conditions, Convolution, Deep learning, attention loss BibRef

Luo, W.F.[Wen-Feng], Yang, M.[Meng], Zheng, W.S.[Wei-Shi],
Weakly-supervised semantic segmentation with saliency and incremental supervision updating,
PR(115), 2021, pp. 107858.
Elsevier DOI 2104
Weakly-supervised, Semantic segmentation, Convolution neural networks BibRef

Yu, C.Q.[Chang-Qian], Shao, Y.J.[Yuan-Jie], Gao, C.X.[Chang-Xin], Sang, N.[Nong],
CondNet: Conditional Classifier for Scene Segmentation,
SPLetters(28), 2021, pp. 758-762.
IEEE DOI 2105
Kernel, Convolution, Feature extraction, Semantics, Training, Task analysis, Aggregates, Conditional classifier, semantic segmentation BibRef

Xu, J.T.[Jiang-Tao], Lu, K.[Kaige], Wang, H.[Han],
Attention fusion network for multi-spectral semantic segmentation,
PRL(146), 2021, pp. 179-184.
Elsevier DOI 2105
Multi-spectral semantic segmentation, Convolutional neural network, Attention mechanism BibRef

Yang, M.Y.[Michael Ying], Kumaar, S.[Saumya], Lyu, Y.[Ye], Nex, F.[Francesco],
Real-time Semantic Segmentation with Context Aggregation Network,
PandRS(178), 2021, pp. 124-134.
Elsevier DOI 2108
Semantic segmentation, Real-time, Convolutional neural network, Context aggregation network BibRef

Li, X.T.[Xiang-Tai], Zhang, L.[Li], Cheng, G.L.[Guang-Liang], Yang, K.Y.[Kui-Yuan], Tong, Y.H.[Yun-Hai], Zhu, X.T.[Xia-Tian], Xiang, T.[Tao],
Global Aggregation Then Local Distribution for Scene Parsing,
IP(30), 2021, pp. 6829-6842.
IEEE DOI 2108
Image segmentation, Adaptation models, Visualization, Convolution, Semantics, Predictive models, Convolutional neural networks, long-range dependencies BibRef

Yang, Z.G.[Zhen-Geng], Yu, H.S.[Hong-Shan], Fu, Q.[Qiang], Sun, W.[Wei], Jia, W.Y.[Wen-Yan], Sun, M.[Mingui], Mao, Z.H.[Zhi-Hong],
NDNet: Narrow While Deep Network for Real-Time Semantic Segmentation,
ITS(22), No. 9, September 2021, pp. 5508-5519.
IEEE DOI 2109
Semantics, Real-time systems, Image segmentation, Computational efficiency, Deep learning, Decoding, Standards, deep learning BibRef

Pan, X.[Xin], Zhao, J.[Jian], Xu, J.[Jun],
Conditional Generative Adversarial Network-Based Training Sample Set Improvement Model for the Semantic Segmentation of High-Resolution Remote Sensing Images,
GeoRS(59), No. 9, September 2021, pp. 7854-7870.
IEEE DOI 2109
Remote sensing, Image segmentation, Training, Semantics, Generative adversarial networks, Feature extraction, Generators, semantic segmentation BibRef


Li, D.[Dehui], Cao, Z.G.[Zhi-Guo], Xian, K.[Ke], Qi, X.Y.[Xin-Yuan], Zhang, C.[Chao], Lu, H.[Hao],
Multi-Direction Convolution for Semantic Segmentation,
ICPR21(519-525)
IEEE DOI 2105
Convolution, Semantics, Benchmark testing, Encoding, Kernel BibRef

Hu, H.[Hanzhe], Cui, J.S.[Jin-Shi], Zha, H.B.[Hong-Bin],
Boundary-aware Graph Convolution for Semantic Segmentation,
ICPR21(1828-1835)
IEEE DOI 2105
Learning systems, Semantics, Benchmark testing, Cognition, Pattern recognition, Task analysis BibRef

Liu, Y.[Yue], Lian, Z.C.[Zhi-Chao],
PSDNet: A Balanced Architecture of Accuracy and Parameters for Semantic Segmentation,
ICPR21(827-834)
IEEE DOI 2105
Interpolation, Convolution, Semantics, Pattern recognition, Task analysis, Semantic Segmentation, D2SUpsample BibRef

Liu, J.B.[Jian-Bo], He, J.J.[Jun-Jun], Qiao, Y.[Yu], Ren, J.S.[Jimmy S.], Li, H.S.[Hong-Sheng],
Learning to Predict Context-adaptive Convolution for Semantic Segmentation,
ECCV20(XXV:769-786).
Springer DOI 2011
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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
Generative Adversarial Network, GAN, Semantic Segmentation .


Last update:Sep 19, 2021 at 21:11:01