Tsao, E.C.K.[Eric Chen-Kuo],
Lin, W.C.[Wei-Chung],
Chen, C.T.[Chin-Tu],
Constraint satisfaction neural networks for image recognition,
PR(26), No. 4, April 1993, pp. 553-567.
Elsevier DOI
0401
BibRef
Earlier: A2, A1, A3:
Constraint Satisfaction Neural Networks for Image Segmentation,
PR(25), No. 7, July 1992, pp. 679-693.
Elsevier DOI
BibRef
Chen, K.S.,
Tsay, D.H.,
Huang, W.P.,
Tzeng, Y.C.,
Remote Sensing Image Segmentation Using a
Kalman Filter-Trained Neural-Network,
IJIST(7), No. 2, Summer 1996, pp. 141-148.
9607
BibRef
Cheng, K.S.[Kuo-Sheng],
Lin, J.S.[Jzau-Sheng],
Mao, C.W.[Chi-Wu],
The Application of Competitive Hopfield Neural Network to Medical Image
Segmentation,
MedImg(15), No. 4, August 1996, pp. 560-567.
IEEE Top Reference.
0203
BibRef
Ziemke, T.,
Radar Image Segmentation Using Recurrent Artificial Neural Networks,
PRL(17), No. 4, April 4 1996, pp. 319-334.
9605
BibRef
Routa, S.[Saroj],
Seethalakshmy, A.G.,
Srivastava, P.[Pramod],
Majumdar, J.[Jharna],
Multimodal Image Segmentation Using a Modified Hopfield Neural Network,
PR(31), No. 6, June 1998, pp. 743-750.
Elsevier DOI
9806
BibRef
Venkatesh, Y.V.,
Rishikesh, N.,
Self-Organizing Neural Networks Based on Spatial Isomorphism for Active
Contour Modeling,
PR(33), No. 7, July 2000, pp. 1239-1250.
Elsevier DOI
0005
BibRef
Venkatesh, Y.V.,
Raja, S.K.,
Ramya, N.,
Multiple contour extraction from graylevel images using an artificial
neural network,
IP(15), No. 4, April 2006, pp. 892-899.
IEEE DOI
0604
BibRef
Gupta, L.[Lalit],
Mangai, U.G.[Utthara Gosa],
Das, S.[Sukhendu],
Integrating region and edge information for texture segmentation using
a modified constraint satisfaction neural network,
IVC(26), No. 8, 1 August 2008, pp. 1106-1117.
Elsevier DOI
0806
Constraint satisfaction neural networks (CSNN); Segmentation;
Texture edge detection; Fuzzy-C means (FCM); Dynamic window
BibRef
Sahami, S.,
Shayesteh, M.G.,
Bi-level image compression technique using neural networks,
IET-IPR(6), No. 5, 2012, pp. 496-506.
DOI Link
1210
BibRef
Längkvist, M.[Martin],
Kiselev, A.[Andrey],
Alirezaie, M.[Marjan],
Loutfi, A.[Amy],
Classification and Segmentation of Satellite Orthoimagery Using
Convolutional Neural Networks,
RS(8), No. 4, 2016, pp. 329.
DOI Link
1604
BibRef
Wei, Y.C.[Yun-Chao],
Liang, X.D.[Xiao-Dan],
Chen, Y.P.[Yun-Peng],
Jie, Z.[Zequn],
Xiao, Y.H.[Yan-Hui],
Zhao, Y.[Yao],
Yan, S.C.[Shui-Cheng],
Learning to segment with image-level annotations,
PR(59), No. 1, 2016, pp. 234-244.
Elsevier DOI
1609
Semantic segmentation. with CNNs
BibRef
Wei, Y.C.[Yun-Chao],
Liang, X.D.[Xiao-Dan],
Chen, Y.P.[Yun-Peng],
Shen, X.,
Cheng, M.M.,
Feng, J.,
Zhao, Y.[Yao],
Yan, S.C.[Shui-Cheng],
STC: A Simple to Complex Framework for Weakly-Supervised Semantic
Segmentation,
PAMI(39), No. 11, November 2017, pp. 2314-2320.
IEEE DOI
1710
Benchmark testing, Image segmentation,
Neural networks, Object detection, Semantics, Training,
Semantic segmentation, convolutional neural network,
weakly-supervised learning
BibRef
Wei, Y.C.[Yun-Chao],
Feng, J.,
Liang, X.D.[Xiao-Dan],
Cheng, M.M.,
Zhao, Y.[Yao],
Yan, S.C.[Shui-Cheng],
Object Region Mining with Adversarial Erasing: A Simple
Classification to Semantic Segmentation Approach,
CVPR17(6488-6496)
IEEE DOI
1711
Head, Heating systems, Image segmentation, Proposals, Semantics, Training
BibRef
Dong, L.[Le],
Feng, N.[Ning],
Zhang, Q.[Qianni],
LSI: Latent semantic inference for natural image segmentation,
PR(59), No. 1, 2016, pp. 282-291.
Elsevier DOI
1609
Image Segmentation
BibRef
Lu, Z.W.[Zhi-Wu],
Fu, Z.Y.[Zhen-Yong],
Xiang, T.[Tao],
Han, P.[Peng],
Wang, L.W.[Li-Wei],
Gao, X.[Xin],
Learning from Weak and Noisy Labels for Semantic Segmentation,
PAMI(39), No. 3, March 2017, pp. 486-500.
IEEE DOI
1702
Computational modeling
BibRef
Li, A.[Aoxue],
Lu, Z.W.[Zhi-Wu],
Wang, L.W.[Li-Wei],
Han, P.[Peng],
Wen, J.R.[Ji-Rong],
Large-Scale Sparse Learning From Noisy Tags for Semantic Segmentation,
Cyber(48), No. 1, January 2018, pp. 253-263.
IEEE DOI
1801
Image segmentation, Matrix decomposition, Noise measurement,
Noise reduction, Semantics, Symmetric matrices, Visualization,
semantic segmentation
BibRef
Cao, Y.,
Shen, C.,
Shen, H.T.,
Exploiting Depth From Single Monocular Images for Object Detection
and Semantic Segmentation,
IP(26), No. 2, February 2017, pp. 836-846.
IEEE DOI
1702
estimation theory
BibRef
Zhang, M.[Mi],
Hu, X.Y.[Xiang-Yun],
Zhao, L.[Like],
Lv, Y.[Ye],
Luo, M.[Min],
Pang, S.Y.[Shi-Yan],
Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation
of High-Resolution Images,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link
1706
BibRef
Ghodrati, A.[Amir],
Diba, A.[Ali],
Pedersoli, M.[Marco],
Tuytelaars, T.[Tinne],
Van Gool, L.J.[Luc J.],
DeepProposals: Hunting Objects and Actions by Cascading Deep
Convolutional Layers,
IJCV(124), No. 2, September 2017, pp. 115-131.
Springer DOI
1708
BibRef
Earlier:
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers,
ICCV15(2578-2586)
IEEE DOI
1602
Aggregates
BibRef
Hong, S.,
Kwak, S.,
Han, B.,
Weakly Supervised Learning with Deep Convolutional Neural Networks
for Semantic Segmentation: Understanding Semantic Layout of Images
with Minimum Human Supervision,
SPMag(34), No. 6, November 2017, pp. 39-49.
IEEE DOI
1712
Benchmark testing, Image recognition, Image segmentation,
Machine learning, Neural networks, Semantics, Visualization
BibRef
Holliday, A.[Andrew],
Barekatain, M.[Mohammadamin],
Laurmaa, J.[Johannes],
Kandaswamy, C.[Chetak],
Prendinger, H.[Helmut],
Speedup of deep learning ensembles for semantic segmentation using a
model compression technique,
CVIU(164), No. 1, 2017, pp. 16-26.
Elsevier DOI
1801
Semantic segmentation
BibRef
Romera, E.,
Álvarez, J.M.,
Bergasa, L.M.,
Arroyo, R.,
ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic
Segmentation,
ITS(19), No. 1, January 2018, pp. 263-272.
IEEE DOI
1801
Computer architecture, Image segmentation, Kernel,
Real-time systems, Semantics,
semantic segmentation
BibRef
Chen, L.C.[Liang-Chieh],
Papandreou, G.[George],
Kokkinos, I.[Iasonas],
Murphy, K.P.[Kevin P.],
Yuille, A.L.[Alan L.],
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
Atrous Convolution, and Fully Connected CRFs,
PAMI(40), No. 4, April 2018, pp. 834-848.
IEEE DOI
1804
BibRef
Earlier: A2, A1, A4, A5, Only:
Weakly-and Semi-Supervised Learning of a Deep Convolutional Network
for Semantic Image Segmentation,
ICCV15(1742-1750)
IEEE DOI
1602
convolution, feature extraction, feedforward neural nets,
image segmentation, learning (artificial intelligence),
semantic segmentation.
Benchmark testing
BibRef
Chen, L.C.[Liang-Chieh],
Zhu, Y.K.[Yu-Kun],
Papandreou, G.[George],
Schroff, F.[Florian],
Adam, H.[Hartwig],
Encoder-Decoder with Atrous Separable Convolution for Semantic Image
Segmentation,
ECCV18(VII: 833-851).
Springer DOI
1810
BibRef
Chen, L.C.,
Barron, J.T.,
Papandreou, G.,
Murphy, K.P.,
Yuille, A.L.,
Semantic Image Segmentation with Task-Specific Edge Detection Using
CNNs and a Discriminatively Trained Domain Transform,
CVPR16(4545-4554)
IEEE DOI
1612
BibRef
Konishi, S.[Scott],
Statistical Cues for Domain Specific Image Segmentation with
Performance Analysis,
CVPR00(I: 125-132).
IEEE DOI
0005
BibRef
Guo, Y.M.[Yan-Ming],
Liu, Y.[Yu],
Georgiou, T.[Theodoros],
Lew, M.S.[Michael S.],
A review of semantic segmentation using deep neural networks,
MultInfoRetr(8), No. 2, June 2018, pp. 87-93.
Springer DOI
1805
Survey, Semantic Segmentation.
BibRef
Nakajima, Y.[Yoshikatsu],
Saito, H.[Hideo],
Simultaneous Object Segmentation and Recognition by Merging CNN Outputs
from Uniformly Distributed Multiple Viewpoints,
IEICE(E101-D), No. 5, May 2018, pp. 1308-1316.
WWW Link.
1805
BibRef
Wang, C.Y.[Chun-Yan],
Xu, A.[Aigong],
Li, X.[Xiaoli],
Supervised Classification High-Resolution Remote-Sensing Image Based
on Interval Type-2 Fuzzy Membership Function,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link
1806
BibRef
Wang, C.Y.[Chun-Yan],
Xu, A.[Aigong],
Li, C.[Chao],
Zhao, X.M.[Xue-Mei],
Interval Type-2 Fuzzy Based Neural Network For High Resolution Remote
Sensing Image Segmentation,
ISPRS16(B7: 385-391).
DOI Link
1610
BibRef
Liu, Z.W.[Zi-Wei],
Li, X.X.[Xiao-Xiao],
Luo, P.[Ping],
Loy, C.C.[Chen Change],
Tang, X.[Xiaoou],
Deep Learning Markov Random Field for Semantic Segmentation,
PAMI(40), No. 8, August 2018, pp. 1814-1828.
IEEE DOI
1807
BibRef
Earlier: A2, A1, A3, A4, A5:
Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via
Deep Layer Cascade,
CVPR17(6459-6468)
IEEE DOI
1711
BibRef
Earlier: A1, A2, A3, A4, A5:
Semantic Image Segmentation via Deep Parsing Network,
ICCV15(1377-1385)
IEEE DOI
1602
Computational modeling, Computer architecture,
Image segmentation, Markov random fields, Neural networks,
convolutional neural network.
Adaptation models, Cows, Real-time systems, Semantics, Training.
Computational efficiency
BibRef
Kang, B.,
Lee, Y.,
Nguyen, T.Q.,
Depth-Adaptive Deep Neural Network for Semantic Segmentation,
MultMed(20), No. 9, September 2018, pp. 2478-2490.
IEEE DOI
1809
convolution, feedforward neural nets, image colour analysis,
image segmentation, learning (artificial intelligence),
deep learning
BibRef
Chen, T.,
Lin, L.,
Wu, X.,
Xiao, N.,
Luo, X.,
Learning to Segment Object Candidates via Recursive Neural Networks,
IP(27), No. 12, December 2018, pp. 5827-5839.
IEEE DOI
1810
Proposals, Merging, Semantics, Feature extraction, Neural networks,
Measurement, Image segmentation, Object proposal generation,
deep learning
BibRef
Kemker, R.[Ronald],
Luu, R.,
Kanan, C.[Christopher],
Low-Shot Learning for the Semantic Segmentation of Remote Sensing
Imagery,
GeoRS(56), No. 10, October 2018, pp. 6214-6223.
IEEE DOI
1810
Feature extraction, Semantics, Image segmentation, Remote sensing,
Image reconstruction, Data models, Support vector machines,
semisupervised
BibRef
Kemker, R.[Ronald],
Salvaggio, C.[Carl],
Kanan, C.[Christopher],
Algorithms for semantic segmentation of multispectral remote sensing
imagery using deep learning,
PandRS(145), 2018, pp. 60-77.
Elsevier DOI
1810
Deep learning, Convolutional neural network,
Semantic segmentation, Multispectral, Unmanned aerial system, Synthetic imagery
BibRef
Chen, L.,
Bentley, P.,
Mori, K.,
Misawa, K.,
Fujiwara, M.,
Rueckert, D.,
DRINet for Medical Image Segmentation,
MedImg(37), No. 11, November 2018, pp. 2453-2462.
IEEE DOI
1811
Image segmentation, Computer architecture, Convolution, Training,
Medical diagnostic imaging, Standards,
abdominal organ segmentation
BibRef
Larsson, M.[Måns],
Arnab, A.[Anurag],
Zheng, S.[Shuai],
Torr, P.H.S.[Philip H.S.],
Kahl, F.[Fredrik],
Revisiting Deep Structured Models for Pixel-Level Labeling with
Gradient-Based Inference,
SIIMS(11), No. 4, 2018, pp. 2610-2628.
DOI Link
1901
BibRef
Zhu, X.O.[Xia-Obin],
Zhang, X.M.[Xin-Ming],
Zhang, X.Y.[Xiao-Yu],
Xue, Z.[Ziyu],
Wang, L.[Lei],
A novel framework for semantic segmentation with generative
adversarial network,
JVCIR(58), 2019, pp. 532-543.
Elsevier DOI
1901
Semantic segmentation, Generative adversarial network (GAN),
Wasserstein distance, Auxiliary higher-order potential loss
BibRef
Roy, A.G.[Abhijit Guha],
Navab, N.[Nassir],
Wachinger, C.[Christian],
Recalibrating Fully Convolutional Networks With Spatial and Channel
'Squeeze and Excitation' Blocks,
MedImg(38), No. 2, February 2019, pp. 540-549.
IEEE DOI
1902
Image segmentation, Biomedical imaging, Decoding, Task analysis,
Encoding, Computer architecture, Retina,
squeeze & excitation
BibRef
Papadomanolaki, M.[Maria],
Vakalopoulou, M.[Maria],
Karantzalos, K.[Konstantinos],
A Novel Object-Based Deep Learning Framework for Semantic
Segmentation of Very High-Resolution Remote Sensing Data: Comparison
with Convolutional and Fully Convolutional Networks,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link
1903
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
Wu, G.M.[Guang-Ming],
Guo, Y.M.[Yi-Min],
Song, X.[Xiaoya],
Guo, Z.L.[Zhi-Ling],
Zhang, H.[Haoran],
Shi, X.D.[Xiao-Dan],
Shibasaki, R.[Ryosuke],
Shao, X.W.[Xiao-Wei],
A Stacked Fully Convolutional Networks with Feature Alignment
Framework for Multi-Label Land-cover Segmentation,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link
1905
BibRef
Wang, Q.,
Yuan, C.,
Liu, Y.,
Learning Deep Conditional Neural Network for Image Segmentation,
MultMed(21), No. 7, July 2019, pp. 1839-1852.
IEEE DOI
1906
Feature extraction, Object segmentation, Visualization,
Brain modeling, Context modeling, Convolutional neural networks,
conditional Boltzmann machines
BibRef
Benjdira, B.[Bilel],
Bazi, Y.[Yakoub],
Koubaa, A.[Anis],
Ouni, K.[Kais],
Unsupervised Domain Adaptation Using Generative Adversarial Networks
for Semantic Segmentation of Aerial Images,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link
1906
BibRef
He, C.[Chu],
Fang, P.[Peizhang],
Zhang, Z.[Zhi],
Xiong, D.[Dehui],
Liao, M.S.[Ming-Sheng],
An End-to-End Conditional Random Fields and Skip-Connected Generative
Adversarial Segmentation Network for Remote Sensing Images,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link
1907
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
Ghosh, S.[Swarnendu],
Das, N.[Nibaran],
Das, I.[Ishita],
Maulik, U.[Ujjwal],
Understanding Deep Learning Techniques for Image Segmentation,
Surveys(52), No. 4, September 2019, pp. Article No 73.
DOI Link
1912
BibRef
Han, Y.M.[Yong-Ming],
Zhang, S.[Shuheng],
Geng, Z.[Zhiqing],
Wei, Q.[Qin],
Ouyang, Z.[Zhi],
Level set based shape prior and deep learning for image segmentation,
IET-IPR(14), No. 1, January 2020, pp. 183-191.
DOI Link
1912
BibRef
Wang, S.[Sherrie],
Chen, W.[William],
Xie, S.M.[Sang Michael],
Azzari, G.[George],
Lobell, D.B.[David B.],
Weakly Supervised Deep Learning for Segmentation of Remote Sensing
Imagery,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link
2001
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
Yu, J.,
Blaschko, M.B.,
The Lovász Hinge: A Novel Convex Surrogate for Submodular Losses,
PAMI(42), No. 3, March 2020, pp. 735-748.
IEEE DOI
2002
Fasteners, Risk management, Optimization, Training,
Complexity theory, Task analysis, Indexes, Lovász extension,
Jaccard index score
BibRef
Berman, M.,
Triki, A.R.,
Blaschko, M.B.,
The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization
of the Intersection-Over-Union Measure in Neural Networks,
CVPR18(4413-4421)
IEEE DOI
1812
Indexes, Loss measurement, Optimization, Image segmentation,
Fasteners, Training, Semantics
BibRef
López, J.[Josué],
Torres, D.[Deni],
Santos, S.[Stewart],
Atzberger, C.[Clement],
Spectral Imagery Tensor Decomposition for Semantic Segmentation of
Remote Sensing Data through Fully Convolutional Networks,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link
2002
BibRef
Chai, D.F.[Deng-Feng],
Newsam, S.[Shawn],
Huang, J.F.[Jing-Feng],
Aerial image semantic segmentation using DCNN predicted distance maps,
PandRS(161), 2020, pp. 309-322.
Elsevier DOI
2002
Deep learning, Semantic segmentation, DCNNs, Distance maps, Distance transform
BibRef
Zhou, L.[Lei],
Kong, X.[Xiangyong],
Gong, C.[Chen],
Zhang, F.[Fan],
Zhang, X.[Xiaoguo],
FC-RCCN: Fully convolutional residual continuous CRF network for
semantic segmentation,
PRL(130), 2020, pp. 54-63.
Elsevier DOI
2002
Continuous conditional random field (C-CRF),
Semantic segmentation, Unary network, Pairwise network
BibRef
Karimi, D.,
Salcudean, S.E.,
Reducing the Hausdorff Distance in Medical Image Segmentation With
Convolutional Neural Networks,
MedImg(39), No. 2, February 2020, pp. 499-513.
IEEE DOI
2002
Image segmentation, Biomedical imaging, Training,
Sensitivity, convolutional neural networks
BibRef
Fu, J.[Jun],
Liu, J.[Jing],
Li, Y.[Yong],
Bao, Y.J.[Yong-Jun],
Yan, W.P.[Wei-Peng],
Fang, Z.W.[Zhi-Wei],
Lu, H.Q.[Han-Qing],
Contextual deconvolution network for semantic segmentation,
PR(101), 2020, pp. 107152.
Elsevier DOI
2003
Semantic segmentation, Deconvolution network,
Channel contextual module, Spatial contextual module
BibRef
López-Cifuentes, A.[Alejandro],
Escudero-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
Li, K.[Kun],
Hu, X.Y.[Xiang-Yun],
Jiang, H.[Huiwei],
Shu, Z.[Zhen],
Zhang, M.[Mi],
Attention-Guided Multi-Scale Segmentation Neural Network for
Interactive Extraction of Region Objects from High-Resolution
Satellite Imagery,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link
2003
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
Gu, X.B.[Xian-Bin],
Deng, J.D.[Jeremiah D.],
A multi-feature bipartite graph ensemble for image segmentation,
PRL(131), 2020, pp. 98-104.
Elsevier DOI
2004
Image segmentation, Feature fusion, Bipartite graph, Spectral clustering
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
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
Lian, Q.,
Duan, L.,
Lv, F.,
Gong, B.,
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
Ding, H.,
Jiang, X.,
Liu, A.Q.,
Thalmann, N.M.,
Wang, G.,
Boundary-Aware Feature Propagation for Scene Segmentation,
ICCV19(6818-6828)
IEEE DOI
2004
feature extraction, graph theory, image segmentation,
learning (artificial intelligence), segment regions,
Convolution
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
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
Raza, H.,
Ravanbakhsh, M.,
Klein, T.,
Nabi, M.,
Weakly Supervised One Shot Segmentation,
MDALC19(1401-1406)
IEEE DOI
2004
image representation, image segmentation,
learning (artificial intelligence), one-shot learning,
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
Ye, L.W.[Lin-Wei],
Rochan, M.[Mrigank],
Liu, Z.[Zhi],
Wang, Y.[Yang],
Cross-Modal Self-Attention Network for Referring Image Segmentation,
CVPR19(10494-10503).
IEEE DOI
2002
BibRef
Cao, J.[Jiale],
Pang, Y.W.[Yan-Wei],
Li, X.L.[Xue-Long],
Triply Supervised Decoder Networks for Joint Detection and Segmentation,
CVPR19(7384-7393).
IEEE DOI
2002
BibRef
Xiong, Y.[Yuwen],
Liao, R.[Renjie],
Zhao, H.S.[Heng-Shuang],
Hu, R.[Rui],
Bai, M.[Min],
Yumer, E.[Ersin],
Urtasun, R.[Raquel],
UPSNet: A Unified Panoptic Segmentation Network,
CVPR19(8810-8818).
IEEE DOI
2002
BibRef
Kirillov, A.[Alexander],
He, K.[Kaiming],
Girshick, R.[Ross],
Rother, C.[Carsten],
Dollar, P.[Piotr],
Panoptic Segmentation,
CVPR19(9396-9405).
IEEE DOI
2002
BibRef
Liu, H.[Huanyu],
Peng, C.[Chao],
Yu, C.Q.[Chang-Qian],
Wang, J.[Jingbo],
Liu, X.[Xu],
Yu, G.[Gang],
Jiang, W.[Wei],
An End-To-End Network for Panoptic Segmentation,
CVPR19(6165-6174).
IEEE DOI
2002
BibRef
Li, Y.W.[Yan-Wei],
Chen, X.[Xinze],
Zhu, Z.[Zheng],
Xie, L.X.[Ling-Xi],
Huang, G.[Guan],
Du, D.L.[Da-Long],
Wang, X.G.[Xin-Gang],
Attention-Guided Unified Network for Panoptic Segmentation,
CVPR19(7019-7028).
IEEE DOI
2002
BibRef
Chen, W.Y.[Wu-Yang],
Jiang, Z.[Ziyu],
Wang, Z.Y.[Zhang-Yang],
Cui, K.[Kexin],
Qian, X.N.[Xiao-Ning],
Collaborative Global-Local Networks for Memory-Efficient Segmentation
of Ultra-High Resolution Images,
CVPR19(8916-8925).
IEEE DOI
2002
BibRef
Zhang, Y.H.[Yi-Heng],
Qiu, Z.[Zhaofan],
Liu, J.G.[Jin-Gen],
Yao, T.[Ting],
Liu, D.[Dong],
Mei, T.[Tao],
Customizable Architecture Search for Semantic Segmentation,
CVPR19(11633-11642).
IEEE DOI
2002
BibRef
Nekrasov, V.[Vladimir],
Chen, H.[Hao],
Shen, C.H.[Chun-Hua],
Reid, I.D.[Ian D.],
Fast Neural Architecture Search of Compact Semantic Segmentation Models
via Auxiliary Cells,
CVPR19(9118-9127).
IEEE DOI
2002
BibRef
Lee, J.[Jungbeom],
Kim, E.[Eunji],
Lee, S.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.[Nuno],
Bidirectional Learning for Domain Adaptation of Semantic Segmentation,
CVPR19(6929-6938).
IEEE DOI
2002
BibRef
Wei, Z.[Zhen],
Zhang, J.Y.[Jing-Yi],
Liu, L.[Li],
Zhu, F.[Fan],
Shen, F.[Fumin],
Zhou, Y.[Yi],
Liu, S.[Si],
Sun, Y.[Yao],
Shao, L.[Ling],
Building Detail-Sensitive Semantic Segmentation Networks With
Polynomial Pooling,
CVPR19(7108-7116).
IEEE DOI
2002
BibRef
He, J.J.[Jun-Jun],
Deng, Z.Y.[Zhong-Ying],
Zhou, L.[Lei],
Wang, Y.[Yali],
Qiao, Y.[Yu],
Adaptive Pyramid Context Network for Semantic Segmentation,
CVPR19(7511-7520).
IEEE DOI
2002
BibRef
Li, H.[Hanchao],
Xiong, P.F.[Peng-Fei],
Fan, H.Q.A.[Hao-Qi-Ang],
Sun, J.[Jian],
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation,
CVPR19(9514-9523).
IEEE DOI
2002
BibRef
Sun, R.[Ruoqi],
Zhu, X.[Xinge],
Wu, C.[Chongruo],
Huang, C.[Chen],
Shi, J.P.[Jian-Ping],
Ma, L.Z.[Li-Zhuang],
Not All Areas Are Equal: Transfer Learning for Semantic Segmentation
via Hierarchical Region Selection,
CVPR19(4355-4364).
IEEE DOI
2002
BibRef
Chen, Y.H.[Yu-Hua],
Li, W.[Wen],
Chen, X.[Xiaoran],
Van Gool, L.J.[Luc J.],
Learning Semantic Segmentation From Synthetic Data: A Geometrically
Guided Input-Output Adaptation Approach,
CVPR19(1841-1850).
IEEE DOI
2002
BibRef
Durall, R.[Ricard],
Pfreundt, F.J.[Franz-Josef],
Köthe, U.[Ullrich],
Keuper, J.[Janis],
Object Segmentation Using Pixel-Wise Adversarial Loss,
GCPR19(303-316).
Springer DOI
1911
BibRef
Wang, Y.,
Zhou, Q.,
Liu, J.,
Xiong, J.,
Gao, G.,
Wu, X.,
Latecki, L.J.,
Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic
Segmentation,
ICIP19(1860-1864)
IEEE DOI
1910
CNN, Lightweight network, Encoder-decoder network, ResNet,
Real-time semantic segmentation
BibRef
Xing, Y.,
Wang, J.,
Chen, X.,
Zeng, G.,
Coupling Two-Stream RGB-D Semantic Segmentation Network by Idempotent
Mappings,
ICIP19(1850-1854)
IEEE DOI
1910
RGB-D Semantic Segmentation, Convoutional Neural Networks
BibRef
Lyu, H.,
Fu, H.,
Hu, X.,
Liu, L.,
Esnet: Edge-Based Segmentation Network for Real-Time Semantic
Segmentation in Traffic Scenes,
ICIP19(1855-1859)
IEEE DOI
1910
Real-Time, Semantic Segmentation, Global Edge Information,
Classification Level Semantic Information
BibRef
Ma, L.Y.[Lei-Yuan],
Liu, Z.Y.[Zi-Yi],
Zheng, N.N.[Nan-Ning],
Wang, J.J.[Jian-Ji],
HAR Enhanced Weakly-Supervised Semantic Segmentation Coupled with
Adversarial Learning,
ICIP19(1845-1849)
IEEE DOI
1910
semantic segmentation, weakly-supervised, adversarial learning, atrous rate
BibRef
Yokoo, S.,
Iizuka, S.,
Fukui, K.,
MLSNet: Resource-Efficient Adaptive Inference with Multi-Level
Segmentation Networks,
ICIP19(1510-1514)
IEEE DOI
1910
semantic segmentation, convolutional network, adaptive inference
BibRef
Liu, M.,
Yin, H.,
Cross Attention Network for Semantic Segmentation,
ICIP19(2434-2438)
IEEE DOI
1910
Semantic segmentation, cross attention, real-time, deep neural networks
BibRef
Bigdeli, S.,
Süsstrunk, S.,
Deep Semantic Segmentation Using NIR as Extra Physical Information,
ICIP19(2439-2443)
IEEE DOI
1910
Deep Semantic Segmentation, Near Infrared, Convolutional Neural Networks
BibRef
Guo, J.,
Markoni, H.,
Image Semantic Segmentation With Edge and Feature Level Attenuators,
ICIP19(2511-2515)
IEEE DOI
1910
ENet, skip connection, attenuator, edge selector, image segmentation
BibRef
Ganti, P.,
Waslander, S.,
Network Uncertainty Informed Semantic Feature Selection for Visual
SLAM,
CRV19(121-128)
IEEE DOI
1908
Simultaneous localization and mapping, Feature extraction,
Uncertainty, Artificial neural networks, Semantics, Entropy,
Semantic Segmentation
BibRef
Hu, T.[Tao],
Dense In Dense: Training Segmentation from Scratch,
ACCV18(VI:454-470).
Springer DOI
1906
BibRef
Türkmen, S.[Sercan],
Heikkilä, J.[Janne],
An Efficient Solution for Semantic Segmentation:
ShuffleNet V2 with Atrous Separable Convolutions,
SCIA19(41-53).
Springer DOI
1906
See also ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.
BibRef
Kim, Y.[Youngeun],
Kim, S.H.[Seung-Hyeon],
Kim, T.[Taekyung],
Kim, C.[Changick],
CNN-Based Semantic Segmentation Using Level Set Loss,
WACV19(1752-1760)
IEEE DOI
1904
convolutional neural nets, entropy, image resolution,
image segmentation, probability, set theory, level set loss,
Training
BibRef
Xiang, W.,
Mao, H.,
Athitsos, V.,
ThunderNet:
A Turbo Unified Network for Real-Time Semantic Segmentation,
WACV19(1789-1796)
IEEE DOI
1904
embedded systems, graphics processing units, image segmentation,
neural nets, Turbo Unified Network, ThunderNet,
Standards
BibRef
Pandey, G.,
Dukkipati, A.,
Learning to Segment With Image-Level Supervision,
WACV19(1856-1865)
IEEE DOI
1904
convolution, image classification, image representation,
image segmentation, learning (artificial intelligence),
Force
BibRef
Karim, R.,
Islam, M.A.,
Bruce, N.D.B.,
Recurrent Iterative Gating Networks for Semantic Segmentation,
WACV19(1070-1079)
IEEE DOI
1904
image segmentation, iterative methods,
learning (artificial intelligence), neural net architecture.
BibRef
Zhang, L.[Liang],
Kong, X.W.[Xiang-Wen],
Shen, P.Y.[Pei-Yi],
Zhu, G.M.[Guang-Ming],
Song, J.[Juan],
Shah, S.A.A.[Syed Afaq Ali],
Bennamoun, M.[Mohammed],
Reflective Field for Pixel-Level Tasks,
ICPR18(529-534)
IEEE DOI
1812
Task analysis, Kernel, Computer architecture, Convolution, Semantics,
Neural networks, Image segmentation
BibRef
Ortiz, A.,
Granados, A.,
Fuentes, O.,
Kiekintveld, C.,
Rosario, D.,
Bell, Z.,
Integrated Learning and Feature Selection for Deep Neural Networks in
Multispectral Images,
PBVS18(1277-127709)
IEEE DOI
1812
Image segmentation, Semantics, Machine learning, Training,
Task analysis, Feature extraction, Neural networks
BibRef
McIntosh, L.,
Maheswaranathan, N.,
Sussillo, D.,
Shlens, J.,
Recurrent Segmentation for Variable Computational Budgets,
EfficientDeep18(1729-172909)
IEEE DOI
1812
Image segmentation, Semantics, Computer architecture,
Computational efficiency, Videos, Computational modeling, Network architecture
BibRef
Xu, X.,
Lu, Q.,
Yang, L.,
Hu, S.,
Chen, D.,
Hu, Y.,
Shi, Y.,
Quantization of Fully Convolutional Networks for Accurate Biomedical
Image Segmentation,
CVPR18(8300-8308)
IEEE DOI
1812
Quantization (signal), Training, Biomedical imaging,
Image segmentation, Uncertainty, Memory management, Neural networks
BibRef
Li, R.,
Li, K.,
Kuo, Y.,
Shu, M.,
Qi, X.,
Shen, X.,
Jia, J.,
Referring Image Segmentation via Recurrent Refinement Networks,
CVPR18(5745-5753)
IEEE DOI
1812
Image segmentation, Semantics, Natural languages, Task analysis,
Feature extraction, Logic gates, Training
BibRef
Briot, A.,
Viswanath, P.,
Yogamani, S.,
Analysis of Efficient CNN Design Techniques for Semantic Segmentation,
ECVW18(776-77609)
IEEE DOI
1812
Convolution, Computer architecture, Semantics, Hardware,
Quantization (signal), Kernel, Computational modeling
BibRef
Sankaranarayanan, S.,
Balaji, Y.,
Jain, A.,
Lim, S.N.,
Chellappa, R.,
Learning from Synthetic Data:
Addressing Domain Shift for Semantic Segmentation,
CVPR18(3752-3761)
IEEE DOI
1812
Task analysis, Semantics, Training, Image reconstruction, Generators,
Image segmentation, Data models
BibRef
Yu, C.,
Wang, J.,
Peng, C.,
Gao, C.,
Yu, G.,
Sang, N.,
Learning a Discriminative Feature Network for Semantic Segmentation,
CVPR18(1857-1866)
IEEE DOI
1812
Semantics, Task analysis, Feature extraction, Convolution,
Computer architecture, Computer vision, Benchmark testing
BibRef
Arnab, A.,
Miksik, O.,
Torr, P.H.S.,
On the Robustness of Semantic Segmentation Models to Adversarial
Attacks,
CVPR18(888-897)
IEEE DOI
1812
Robustness, Semantics, Image segmentation, Perturbation methods,
Task analysis, Neural networks, Training
BibRef
Wang, X.,
You, S.,
Li, X.,
Ma, H.,
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common
Object Features,
CVPR18(1354-1362)
IEEE DOI
1812
Image segmentation, Semantics, Training, Heating systems,
Task analysis, Feature extraction, Computer vision
BibRef
Shen, T.,
Lin, G.,
Shen, C.,
Reid, I.D.,
Bootstrapping the Performance of Webly Supervised Semantic
Segmentation,
CVPR18(1363-1371)
IEEE DOI
1812
Training, Image segmentation, Semantics, Knowledge engineering,
Noise measurement, Bidirectional control, Estimation
BibRef
Zhang, Z.,
Xie, C.,
Wang, J.,
Xie, L.,
Yuille, A.L.,
DeepVoting: A Robust and Explainable Deep Network for Semantic Part
Detection Under Partial Occlusion,
CVPR18(1372-1380)
IEEE DOI
1812
Semantics, Visualization, Training, Wheels, Proposals,
Feature extraction, Object detection
BibRef
Zlateski, A.,
Jaroensri, R.,
Sharma, P.,
Durand, F.,
On the Importance of Label Quality for Semantic Segmentation,
CVPR18(1479-1487)
IEEE DOI
1812
Computer vision, Pattern recognition
BibRef
Zhang, Y.,
Qiu, Z.,
Yao, T.,
Liu, D.,
Mei, T.,
Fully Convolutional Adaptation Networks for Semantic Segmentation,
CVPR18(6810-6818)
IEEE DOI
1812
Semantics, Image segmentation, Adaptation models, Visualization,
Task analysis, Games, Videos
BibRef
Saleh, F.S.[Fatemeh Sadat],
Aliakbarian, M.S.[Mohammad Sadegh],
Salzmann, M.[Mathieu],
Petersson, L.[Lars],
Alvarez, J.M.[Jose M.],
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation,
ECCV18(II: 86-103).
Springer DOI
1810
BibRef
Marin, D.[Dmitrii],
Tang, M.[Meng],
Ben Ayed, I.[Ismail],
Boykov, Y.[Yuri],
Beyond Gradient Descent for Regularized Segmentation Losses,
CVPR19(10179-10188).
IEEE DOI
2002
BibRef
Tang, M.[Meng],
Perazzi, F.[Federico],
Djelouah, A.[Abdelaziz],
Ben Ayed, I.[Ismail],
Schroers, C.[Christopher],
Boykov, Y.[Yuri],
On Regularized Losses for Weakly-supervised CNN Segmentation,
ECCV18(XVI: 524-540).
Springer DOI
1810
BibRef
Hu, T.[Tao],
Wang, Y.[Yao],
Chen, Y.S.[Yi-Song],
Lu, P.[Peng],
Wang, H.[Heng],
Wang, G.P.[Guo-Ping],
Sobel Heuristic Kernel for Aerial Semantic Segmentation,
ICIP18(3074-3078)
IEEE DOI
1809
Kernel, Semantics, Image segmentation, Image edge detection,
Neural networks, Detectors, Convolution, Semantic Segmentation, Edge Detection
BibRef
Siam, M.,
Gamal, M.,
Abdel-Razek, M.,
Yogamani, S.,
Jagersand, M.,
Zhang, H.,
A Comparative Study of Real-Time Semantic Segmentation for Autonomous
Driving,
ECVW18(700-70010)
IEEE DOI
1812
Convolution, Semantics, Computer architecture, Decoding,
Context modeling, Real-time systems, Image segmentation
BibRef
Siam, M.,
Gamal, M.,
Abdel-Razek, M.,
Yogamani, S.,
Jagersand, M.,
RTSeg: Real-Time Semantic Segmentation Comparative Study,
ICIP18(1603-1607)
IEEE DOI
1809
Computer architecture, Convolution, Semantics, Decoding,
Feature extraction, Benchmark testing, Real-time systems, realtime,
benchmarking framework
BibRef
Feng, Z.,
Yong, H.,
Xukun, S.,
GRANet: Global Refinement Atrous Convolutional Neural Network for
Semantic Scene Segmentation,
ICIP18(1568-1572)
IEEE DOI
1809
Semantics, Feature extraction, Convolution, Image segmentation,
Task analysis, Training, Convolutional neural networks,
Global Context
BibRef
Yang, W.,
Zhou, Q.,
Lu, J.,
Wu, X.,
Zhang, S.,
Latecki, L.J.,
Dense Deconvolutional Network for Semantic Segmentation,
ICIP18(1573-1577)
IEEE DOI
1809
Image segmentation, Training, Semantics, Decoding, Convolution,
Deconvolution, Feature extraction, Semantic Segmentation, FCNs
BibRef
Oudni, L.,
Vázquez, C.,
Coulombe, S.,
Motion Occlusions for Automatic Generation of Relative Depth Maps,
ICIP18(1538-1542)
IEEE DOI
1809
Optical imaging, Integrated optics, Image color analysis,
Estimation, Coherence, Interpolation, Image segmentation,
BibRef
Huang, Q.,
Xia, C.,
Li, S.,
Wang, Y.,
Song, Y.,
Kuo, C.C.J.,
Unsupervised Clustering Guided Semantic Segmentation,
WACV18(1489-1498)
IEEE DOI
1806
feature extraction, feedforward neural nets,
image classification, image representation, image segmentation,
Training
BibRef
Nigam, I.,
Huang, C.,
Ramanan, D.,
Ensemble Knowledge Transfer for Semantic Segmentation,
WACV18(1499-1508)
IEEE DOI
1806
image segmentation, learning (artificial intelligence),
aerial drone robotics, aerial scenes, aerial segmentation,
Visualization
BibRef
Richmond, D.[David],
Kainmueller, D.[Dagmar],
Yang, M.[Michael],
Myers, E.[Eugene],
Rother, C.[Carsten],
Mapping Auto-context Decision Forests to Deep ConvNets for Semantic
Segmentation,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Nekrasov, V.[Vladimir],
Ju, J.[Janghoon],
Choi, J.[Jaesik],
Global Deconvolutional Networks for Semantic Segmentation,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Jiang, Y.,
Chi, Z.,
A Fully-Convolutional Framework for Semantic Segmentation,
DICTA17(1-7)
IEEE DOI
1804
image classification, image segmentation,
learning (artificial intelligence), deep learning technique,
Semantics
BibRef
Fu, J.,
Liu, J.,
Wang, Y.,
Lu, H.,
Densely connected deconvolutional network for semantic segmentation,
ICIP17(3085-3089)
IEEE DOI
1803
Convergence, Image segmentation, Semantics, Spatial resolution,
Stacking, Training, Deconvolutional Network, Dense Connection,
Semantic Segmentation
BibRef
Chu, J.,
Xiao, X.,
Meng, G.,
Wang, L.,
Pan, C.,
Learnable contextual regularization for semantic segmentation of
indoor scene images,
ICIP17(1267-1271)
IEEE DOI
1803
Computer architecture, Convolution, Image segmentation, Kernel,
Semantics, Task analysis, Training, Contextual constraints,
Semantic segmentation
BibRef
Liu, Y.,
Lew, M.S.,
Improving the discrimination between foreground and background for
semantic segmentation,
ICIP17(1272-1276)
IEEE DOI
1803
Computational modeling, Image segmentation, Proposals, Semantics,
Standards, Task analysis, Training, Fully Convolutional Networks,
Semantic Segmentation
BibRef
Qi, X.,
Liao, R.,
Jia, J.,
Fidler, S.,
Urtasun, R.,
3D Graph Neural Networks for RGBD Semantic Segmentation,
ICCV17(5209-5218)
IEEE DOI
1802
feature extraction, graph theory, image classification,
image representation, image segmentation,
BibRef
Souly, N.,
Spampinato, C.,
Shah, M.,
Semi Supervised Semantic Segmentation Using Generative Adversarial
Network,
ICCV17(5689-5697)
IEEE DOI
1802
feature extraction, image classification, image segmentation,
learning (artificial intelligence), semantic networks,
Visualization
BibRef
Sickert, S.[Sven],
Denzler, J.[Joachim],
Semantic Segmentation of Outdoor Areas Using 3D Moment Invariants and
Contextual Cues,
GCPR17(165-176).
Springer DOI
1711
BibRef
Morley, D.,
Foroosh, H.,
Improving RANSAC-Based Segmentation through CNN Encapsulation,
CVPR17(2661-2670)
IEEE DOI
1711
Encapsulation, Feature extraction,
Image edge detection, Image segmentation, Training
BibRef
Ke, T.W.,
Maire, M.,
Yu, S.X.,
Multigrid Neural Architectures,
CVPR17(4067-4075)
IEEE DOI
1711
Computer architecture, Convolution, Image segmentation, Routing,
Semantics, Standards
BibRef
Cohen, G.,
Weinshall, D.,
Hidden Layers in Perceptual Learning,
CVPR17(5349-5357)
IEEE DOI
1711
Biological system modeling, Computational modeling, Convolution,
Image segmentation, Training, Visualization
BibRef
Roy, A.[Anirban],
Todorovic, S.[Sinisa],
Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly
Supervised Image Segmentation,
CVPR17(7282-7291)
IEEE DOI
1711
BibRef
Earlier:
A Multi-scale CNN for Affordance Segmentation in RGB Images,
ECCV16(IV: 186-201).
Springer DOI
1611
Gaussian distribution, Image segmentation, Labeling, Neurons,
Semantics, Training, Visualization
BibRef
Nissen, M.S.[Malte S.],
Krause, O.[Oswin],
Almstrup, K.[Kristian],
Kjærulff, S.[Søren],
Nielsen, T.T.[Torben T.],
Nielsen, M.[Mads],
Convolutional Neural Networks for Segmentation and Object Detection of
Human Semen,
SCIA17(I: 397-406).
Springer DOI
1706
BibRef
Schneider, L.[Lukas],
Jasch, M.[Manuel],
Fröhlich, B.[Björn],
Weber, T.[Thomas],
Franke, U.[Uwe],
Pollefeys, M.[Marc],
Rätsch, M.[Matthias],
Multimodal Neural Networks: RGB-D for Semantic Segmentation and Object
Detection,
SCIA17(I: 98-109).
Springer DOI
1706
BibRef
García, G.M.,
Husain, F.,
Schulz, H.,
Frintrop, S.,
Torras, C.,
Behnke, S.,
Semantic segmentation priors for object discovery,
ICPR16(549-554)
IEEE DOI
1705
Computer vision, Image color analysis, Image segmentation,
Neural networks, Proposals, Semantics
BibRef
Wang, C.,
Yu, J.,
Mauch, L.,
Yang, B.,
Binary Segmentation Based Class Extension in Semantic Image
Segmentation Using Convolutional Neural Networks,
ICIP18(2232-2236)
IEEE DOI
1809
Image segmentation, Semantics, Training, Task analysis,
Computational modeling, Manuals, Convolutional neural networks,
convolutional neural networks
BibRef
Wang, C.,
Mauch, L.,
Guo, Z.,
Yang, B.,
On semantic image segmentation using deep convolutional neural
network with shortcuts and easy class extension,
IPTA16(1-6)
IEEE DOI
1703
image segmentation
BibRef
Mousavian, A.,
Pirsiavash, H.,
KošeckŽá, J.,
Joint Semantic Segmentation and Depth Estimation with Deep
Convolutional Networks,
3DV16(611-619)
IEEE DOI
1701
Computer architecture
BibRef
Cannici, M.[Marco],
Ciccone, M.[Marco],
Romanoni, A.[Andrea],
Matteucci, M.[Matteo],
Attention Mechanisms for Object Recognition With Event-Based Cameras,
WACV19(1127-1136)
IEEE DOI
1904
cameras, image recognition, image sequences, neural nets,
object recognition, object recognition, event-based cameras,
Object recognition
BibRef
Visin, F.,
Romero, A.,
Cho, K.,
Matteucci, M.,
Ciccone, M.,
Kastner, K.,
Bengio, Y.,
Courville, A.,
ReSeg: A Recurrent Neural Network-Based Model for Semantic
Segmentation,
DeepLearn-C16(426-433)
IEEE DOI
1612
BibRef
Jarrar, M.,
Kerkeni, A.,
Abdallah, A.B.,
Bedoui, M.H.,
MLP Neural Network Classifier for Medical Image Segmentation,
CGiV16(88-93)
IEEE DOI
1608
image classification
BibRef
Hernández, J.[Juanita],
Gómez, W.[Wilfrido],
Automatic Tuning of the Pulse-Coupled Neural Network Using Differential
Evolution for Image Segmentation,
MCPR16(157-166).
Springer DOI
1608
BibRef
Murdock, C.[Calvin],
de la Torre, F.[Fernando],
Additive Component Analysis,
CVPR17(673-681)
IEEE DOI
1711
BibRef
Earlier:
Semantic Component Analysis,
ICCV15(1484-1492)
IEEE DOI
1602
Additives, Image reconstruction, Kernel, Machine learning, Manifolds,
Optimization, Principal component analysis.
Feature extraction. decomposition of images into semantic components.
BibRef
Noh, H.,
Hong, S.,
Han, B.,
Learning Deconvolution Network for Semantic Segmentation,
ICCV15(1520-1528)
IEEE DOI
1602
Deconvolution
BibRef
Lin, D.,
Dai, J.,
Jia, J.,
He, K.,
Sun, J.,
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic
Segmentation,
CVPR16(3159-3167)
IEEE DOI
1612
BibRef
Dai, J.,
He, K.,
Sun, J.,
Instance-Aware Semantic Segmentation via Multi-task Network Cascades,
CVPR16(3150-3158)
IEEE DOI
1612
BibRef
And:
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks
for Semantic Segmentation,
ICCV15(1635-1643)
IEEE DOI
1602
Erbium
BibRef
Pathak, D.,
Krahenbuhl, P.,
Darrell, T.J.,
Constrained Convolutional Neural Networks for Weakly Supervised
Segmentation,
ICCV15(1796-1804)
IEEE DOI
1602
Convolutional codes
BibRef
Safar, S.[Simon],
Yang, M.H.[Ming-Hsuan],
Learning shape priors for object segmentation via neural networks,
ICIP15(1835-1839)
IEEE DOI
1512
Object segmentation; convolutional neural networks; shape priors
BibRef
Gidaris, S.[Spyridon],
Komodakis, N.[Nikos],
Attend Refine Repeat: Active Box Proposal Generation via In-Out
Localization,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
And:
LocNet: Improving Localization Accuracy for Object Detection,
CVPR16(789-798)
IEEE DOI
1612
BibRef
Earlier:
Object Detection via a Multi-region and Semantic Segmentation-Aware
CNN Model,
ICCV15(1134-1142)
IEEE DOI
1602
Biological system modeling
BibRef
Porzi, L.[Lorenzo],
Rota Bulo, S.[Samuel],
Colovic, A.[Aleksander],
Kontschieder, P.[Peter],
Seamless Scene Segmentation,
CVPR19(8269-8278).
IEEE DOI
2002
BibRef
Rota Bulo, S.[Samuel],
Kontschieder, P.[Peter],
Neural Decision Forests for Semantic Image Labelling,
CVPR14(81-88)
IEEE DOI
1409
neural network; random forest; semantic image labelling
BibRef
Masci, J.[Jonathan],
Giusti, A.[Alessandro],
Ciresan, D.C.[Dan C.],
Fricout, G.[Gabriel],
Schmidhuber, J.[Jurgen],
A fast learning algorithm for image segmentation with max-pooling
convolutional networks,
ICIP13(2713-2717)
IEEE DOI
1402
Convolutional Network
BibRef
Giusti, A.[Alessandro],
Ciresan, D.C.[Dan C.],
Masci, J.[Jonathan],
Gambardella, L.M.[Luca M.],
Schmidhuber, J.[Jurgen],
Fast image scanning with deep max-pooling convolutional neural
networks,
ICIP13(4034-4038)
IEEE DOI
1402
Biomedical Imaging
BibRef
del Campo-Becerra, G.D.M.[Gustavo D. Martín],
Yañez-Vargas, J.I.[Juan I.],
López-Ruíz, J.A.[Josué A.],
Texture Analysis of Mean Shift Segmented Low-Resolution
Speckle-Corrupted Fractional SAR Imagery through Neural Network
Classification,
CASI14(998-1005).
Springer DOI
1411
BibRef
Yazdanpanah, A.P.[Ali Pour],
Regentova, E.E.[Emma E.],
Mandava, A.K.[Ajay Kumar],
Ahmad, T.[Touqeer],
Sky Segmentation by Fusing Clustering with Neural Networks,
ISVC13(II:663-672).
Springer DOI
1311
BibRef
Andersen, J.D.[Jens D.],
Image Decomposition by Radial Basis Functions,
SCIA03(749-754).
Springer DOI
0310
BibRef
Matsui, K.[Kazuhiro],
Kosugi, Y.[Yukio],
Image Segmentation by Neural-net Classifiers with Genetic Selection of
Feature Indices,
ICIP99(I:524-528).
IEEE DOI
BibRef
9900
Zong, X.,
Meyer-Baese, A., and
Laine, A.,
Multiscale Segmentation Through a Radial Basis Neural Network,
ICIP97(III: 400-403).
IEEE DOI
BibRef
9700
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Other Complete Systems .