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, Convolution,
Feature extraction, Machine learning, Object detection, Visualization
BibRef
Li, L.[Long],
Liu, N.[Nian],
Zhang, D.W.[Ding-Wen],
Li, Z.Y.[Zhong-Yu],
Khan, S.[Salman],
Anwer, R.[Rao],
Cholakkal, H.[Hisham],
Han, J.W.[Jun-Wei],
Khan, F.S.[Fahad Shahbaz],
CONDA: Condensed Deep Association Learning for Co-Salient Object
Detection,
ECCV24(L: 287-303).
Springer DOI
2412
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
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, L.[Li],
Song, X.[Xiang],
He, Y.H.[Yu-Hang],
Wang, C.X.[Chang-Xin],
Dong, S.L.[Song-Lin],
Wei, X.[Xing],
Gong, Y.H.[Yi-Hong],
Domain Incremental Object Detection Based on Feature Space Topology
Preserving Strategy,
CirSysVideo(34), No. 1, January 2024, pp. 424-437.
IEEE DOI
2401
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.H.[Chun-Hua],
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
Yin, W.[Wei],
Liu, Y.F.[Yi-Fan],
Shen, C.H.[Chun-Hua],
Sun, B.C.[Bai-Chuan],
van den Hengel, A.J.[Anton J.],
Scaling Up Multi-domain Semantic Segmentation with Sentence Embeddings,
IJCV(132), No. 1, January 2024, pp. 4036-4051.
Springer DOI
2409
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
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.L.[Jia-Le],
Pang, Y.W.[Yan-Wei],
Zhao, S.J.[Sheng-Jie],
Li, X.L.[Xue-Long],
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
Nie, J.[Jie],
Wang, C.L.[Cheng-Long],
Yu, S.S.[Shu-Song],
Shi, J.J.[Jin-Jin],
Lv, X.W.[Xiao-Wei],
Wei, Z.Q.[Zhi-Qiang],
MIGN: Multiscale Image Generation Network for Remote Sensing Image
Semantic Segmentation,
MultMed(25), 2023, pp. 5601-5613.
IEEE DOI
2311
BibRef
Wang, C.L.[Cheng-Long],
Wu, D.[Dong],
Nie, J.[Jie],
Huang, L.[Lei],
R2SN: Refined Semantic Segmentation Network of City Remote Sensing
Image,
IUC20(380-396).
Springer DOI
2103
City scale vehicle detection.
BibRef
Gao, G.W.[Guang-Wei],
Xu, G.[Guoan],
Yu, Y.[Yi],
Xie, J.[Jin],
Yang, J.[Jian],
Yue, D.[Dong],
MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for
Real-Time Semantic Segmentation,
ITS(23), No. 12, December 2022, pp. 25489-25499.
IEEE DOI
2212
Convolution, Semantics, Ear, Real-time systems, Image segmentation,
Feature extraction, Task analysis, context fusion
BibRef
Xie, B.[Bin],
Cao, J.[Jiale],
Anwer, R.M.[Rao Muhammad],
Xie, J.[Jin],
Nie, J.[Jing],
Yang, A.[Aiping],
Pang, Y.W.[Yan-Wei],
Multi-query and multi-level enhanced network for semantic
segmentation,
PR(156), 2024, pp. 110777.
Elsevier DOI
2408
Semantic segmentation, Transformer, Multi-query, Multi-level
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.W.[Hai-Wei],
Zhou, Q.H.[Qiu-Hao],
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
Zhang, H.[Hua],
Jiang, Z.G.[Zhen-Gang],
Xu, J.[Jun],
Yao, X.K.[Xue-Kun],
Semantic segmentation of ultra-high resolution remote sensing images
based on fully convolutional neural networks,
CVIDL23(159-165)
IEEE DOI
2403
Training, Measurement, Semantic segmentation,
Computational modeling, Neural networks, Feature extraction,
fully convolutional neural networks
BibRef
Liu, Y.[Yan],
Zhang, Y.Z.[Yun-Zhou],
Liu, S.C.[Shi-Chang],
Coleman, S.[Sonya],
Wang, Z.Y.[Zhen-Yu],
Qiu, F.[Feng],
Salient Object Detection by Aggregating Contextual Information,
PRL(153), 2022, pp. 190-199.
Elsevier DOI
2201
Salient object detection, Convolutional neural network,
Aggregating contextual information
BibRef
Lin, D.[Di],
Shen, D.G.[Ding-Guo],
Ji, Y.F.[Yuan-Feng],
Shen, S.T.[Si-Ting],
Xie, M.R.[Ming-Rui],
Feng, W.[Wei],
Huang, H.[Hui],
TAGNet: Learning Configurable Context Pathways for Semantic
Segmentation,
PAMI(45), No. 2, February 2023, pp. 2475-2491.
IEEE DOI
2301
Context modeling, Deformable models, Semantics,
Computational modeling, Image segmentation, Kernel, Correlation,
convolutional neural networks
BibRef
Yuan, F.N.[Fei-Niu],
Li, K.[Kang],
Wang, C.M.[Chun-Mei],
Fang, Z.J.[Zhi-Jun],
A lightweight network for smoke semantic segmentation,
PR(137), 2023, pp. 109289.
Elsevier DOI
2302
Smoke semantic segmentation, Deep learning,
Attention mechanism, Lightweight network, Channel split and shuffle
BibRef
Artola, A.[Aitor],
Semantic Segmentation: A Zoology of Deep Architectures,
IPOL(13), 2023, pp. 167-182.
DOI Link
2306
Code, Semantic Segmentation.
BibRef
Babiloni, F.[Francesca],
Marras, I.[Ioannis],
Deng, J.K.[Jian-Kang],
Kokkinos, F.[Filippos],
Maggioni, M.[Matteo],
Chrysos, G.[Grigorios],
Torr, P.[Philip],
Zafeiriou, S.P.[Stefanos P.],
Linear Complexity Self-Attention With 3rd Order Polynomials,
PAMI(45), No. 11, November 2023, pp. 12726-12737.
IEEE DOI
2310
BibRef
Earlier: A1, A2, A4, A3, A6, A8, Only:
Babiloni, F.[Francesca],
Marras, I.[Ioannis],
Kokkinos, F.[Filippos],
Deng, J.K.[Jian-Kang],
Chrysos, G.[Grigorios],
Zafeiriou, S.P.[Stefanos P.],
Poly-NL: Linear Complexity Non-local Layers With 3rd Order
Polynomials,
ICCV21(10498-10508)
IEEE DOI
2203
Image segmentation, Image recognition, Limiting, Complexity theory,
Face detection, Convolutional neural networks, Task analysis, Faces
BibRef
Singha, T.[Tanmay],
Pham, D.S.[Duc-Son],
Krishna, A.[Aneesh],
Improved Short-term Dense Bottleneck network for efficient scene
analysis,
CVIU(235), 2023, pp. 103795.
Elsevier DOI
2310
Semantic segmentation, Convolutional neural network,
Scene understanding, Real-time, Machine learning, Encoder-decoder
BibRef
Chen, S.J.[Sheng-Jia],
Yang, X.[Xiwei],
Li, Z.X.[Zhi-Xin],
Improving semantic segmentation with knowledge reasoning network,
JVCIR(96), 2023, pp. 103923.
Elsevier DOI
2310
Semantic segmentation, Knowledge reasoning,
Graph convolutional network, External knowledge, Context information
BibRef
Zhong, X.F.[Xin-Fang],
Kuang, W.[Wenlan],
Li, Z.X.[Zhi-Xin],
Adaptive graph reasoning network for object detection,
IVC(151), 2024, pp. 105248.
Elsevier DOI
2411
Object detection, Semantic relationship, Relation mining,
Feature enhancement, Relation graph reasoning
BibRef
Abate, A.F.[Andrea F.],
Cimmino, L.[Lucia],
Lorenzo-Navarro, J.[Javier],
An ablation study on part-based face analysis using a Multi-input
Convolutional Neural Network and Semantic Segmentation,
PRL(173), 2023, pp. 45-49.
Elsevier DOI
2310
Multi-input CNN, Face analysis, Deep learning
BibRef
Zhang, Y.X.[Yi-Xin],
Mazurowski, M.A.[Maciej A.],
Convolutional neural networks rarely learn shape for semantic
segmentation,
PR(146), 2024, pp. 110018.
Elsevier DOI
2311
Segmentation, Feature measurement, Machine learning, Computer vision
BibRef
Xu, G.A.[Guo-An],
Li, J.C.[Jun-Cheng],
Gao, G.W.[Guang-Wei],
Lu, H.M.[Hui-Min],
Yang, J.[Jian],
Yue, D.[Dong],
Lightweight Real-Time Semantic Segmentation Network with Efficient
Transformer and CNN,
ITS(24), No. 12, December 2023, pp. 15897-15906.
IEEE DOI Code:
WWW Link.
2312
BibRef
Xu, G.[Guoan],
Jia, W.J.[Wen-Jing],
Wu, T.[Tao],
Chen, L.G.[Li-Geng],
Gao, G.W.[Guang-Wei],
HAFormer: Unleashing the Power of Hierarchy-Aware Features for
Lightweight Semantic Segmentation,
IP(33), 2024, pp. 4202-4214.
IEEE DOI Code:
WWW Link.
2408
BibRef
Liu, B.[Bing],
Gao, Y.S.[Yan-Sheng],
Li, H.[Hai],
Zhong, Z.H.[Zhao-Hao],
Zhao, H.W.[Hong-Wei],
Lite-weight semantic segmentation with AG self-attention,
IET-CV(18), No. 1, 2024, pp. 72-83.
DOI Link
2403
computational complexity, convolutional neural nets
BibRef
Coupeau, P.[Patty],
Fasquel, J.B.[Jean-Baptiste],
Dinomais, M.[Mickaël],
On the use of GNN-based structural information to improve CNN-based
semantic image segmentation,
JVCIR(101), 2024, pp. 104167.
Elsevier DOI
2406
Image segmentation, Structural information,
Node classification, Graph neural network, Graph coarsening
BibRef
Chen, L.[Ling],
Tang, Z.[Zedong],
Li, H.[Hao],
Improving CNN-based semantic segmentation on structurally similar
data using contrastive graph convolutional networks,
PR(155), 2024, pp. 110622.
Elsevier DOI
2408
Semantic segmentation, Structural similarity, Graph network,
Contrastive learning
BibRef
Ma, Y.D.[Ying-Dong],
Hu, X.Y.[Xiao-Yu],
TFRNet: Semantic Segmentation Network with Token Filtration and
Refinement Method,
MultMed(26), 2024, pp. 8242-8254.
IEEE DOI
2408
Filtration, Current transformers, Semantic segmentation,
Task analysis, Semantics, Feature extraction, Convolution,
vision transformer
BibRef
Bai, S.[Shuang],
Liang, C.[Chen],
Wang, Z.[Zhen],
Pan, W.C.[Wen-Chao],
Information entropy induced graph convolutional network for semantic
segmentation,
JVCIR(103), 2024, pp. 104217.
Elsevier DOI
2409
Semantic segmentation, Graph convolutional network,
Information entropy, Contextual information
BibRef
Zheng, X.[Xu],
Luo, Y.H.[Yun-Hao],
Zhou, P.Y.[Peng-Yuan],
Wang, L.[Lin],
Distilling efficient Vision Transformers from CNNs for semantic
segmentation,
PR(158), 2025, pp. 111029.
Elsevier DOI Code:
WWW Link.
2411
Knowledge distillation, Vision transformer,
Convolutional neural networks, Semantic segmentation
BibRef
Chen, H.H.[Hong-Hao],
Chu, X.X.[Xiang-Xiang],
Ren, Y.J.[Yong-Jian],
Zhao, X.[Xin],
Huang, K.Q.[Kai-Qi],
PeLK: Parameter-Efficient Large Kernel ConvNets with Peripheral
Convolution,
CVPR24(5557-5567)
IEEE DOI
2410
Convolution, Semantic segmentation, Object detection,
Performance gain, Transformers, Complexity theory, Large Kernel
BibRef
Xiong, Y.[Yuwen],
Li, Z.Q.[Zhi-Qi],
Chen, Y.T.[Yun-Tao],
Wang, F.[Feng],
Zhu, X.[Xizhou],
Luo, J.P.[Jia-Peng],
Wang, W.H.[Wen-Hai],
Lu, T.[Tong],
Li, H.S.[Hong-Sheng],
Qiao, Y.[Yu],
Lu, L.W.[Le-Wei],
Zhou, J.[Jie],
Dai, J.F.[Ji-Feng],
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator
for Vision Applications,
CVPR24(5652-5661)
IEEE DOI
2410
Convolution, Image synthesis, Semantic segmentation,
Computer architecture, Aerodynamics, Diffusion models
BibRef
Zampokas, G.[Georgios],
Bouganis, C.S.[Christos-Savvas],
Tzovaras, D.[Dimitrios],
Latency Driven Spatially Sparse Optimization for Multi-Branch CNNs
for Semantic Segmentation,
LLVMCrive24(949-957)
IEEE DOI
2404
Performance evaluation, Location awareness,
Semantic segmentation, Computer architecture, Hardware
BibRef
Du, W.C.[Wen-Chao],
Yang, H.T.[Hao-Tian],
Toe, T.T.[Teoh Teik],
An improved image segmentation model of FCN based on residual network,
CVIDL23(135-139)
IEEE DOI
2403
Training, Deep learning, Image segmentation, Image recognition,
Costs, Neural networks, Object detection,
Residual Neural Network Image Seg-m entation Fully Convolutional Networks
BibRef
Zhu, J.J.[Jin-Jing],
Luo, Y.H.[Yun-Hao],
Zheng, X.[Xu],
Wang, H.[Hao],
Wang, L.[Lin],
A Good Student is Cooperative and Reliable: CNN-Transformer
Collaborative Learning for Semantic Segmentation,
ICCV23(11686-11696)
IEEE DOI
2401
BibRef
Gao, R.[Roland],
Rethinking Dilated Convolution for Real-time Semantic Segmentation,
ECV23(4675-4684)
IEEE DOI
2309
BibRef
Goan, E.[Ethan],
Fookes, C.[Clinton],
Uncertainty in Real-Time Semantic Segmentation on Embedded Systems,
EVW23(4491-4501)
IEEE DOI
2309
BibRef
Zhou, J.X.[Jing-Xing],
Beyerer, J.[Jürgen],
Category Differences Matter: A Broad Analysis of Inter-Category Error
in Semantic Segmentation,
SAIAD23(3870-3880)
IEEE DOI
2309
BibRef
Koenig, A.[Alexander],
Schambach, M.[Maximilian],
Otterbach, J.[Johannes],
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic
Segmentation,
SAIAD23(3789-3798)
IEEE DOI
2309
BibRef
Lambert, Z.[Zoé],
Le Guyader, C.[Carole],
Petitjean, C.[Caroline],
On the Inclusion of Topological Requirements in CNNs for Semantic
Segmentation Applied to Radiotherapy,
SSVM23(363-375).
Springer DOI
2307
BibRef
Pastorino, M.[Martina],
Moser, G.[Gabriele],
Serpico, S.B.[Sebastiano B.],
Zerubia, J.[Josiane],
Fully Convolutional and Feedforward Networks for The Semantic
Segmentation of Remotely Sensed Images,
ICIP22(1876-1880)
IEEE DOI
2211
Satellites, Image resolution, Computational modeling, Urban areas,
Semantics, Feedforward neural networks, Labeling, CNN, FCN,
multiresolution satellite images
BibRef
Giraldo, J.H.[Jhony H.],
Scarrica, V.[Vincenzo],
Staiano, A.[Antonino],
Camastra, F.[Francesco],
Bouwmans, T.[Thierry],
Hypergraph Convolutional Networks for Weakly-Supervised Semantic
Segmentation,
ICIP22(16-20)
IEEE DOI
2211
Deep learning, Convolution, Computational modeling, Semantics,
Feature extraction, Semantic segmentation,
hypergraph convolutional networks
BibRef
Li, L.L.[Liu-Lei],
Zhou, T.F.[Tian-Fei],
Wang, W.G.[Wen-Guan],
Li, J.W.[Jian-Wu],
Yang, Y.[Yi],
Deep Hierarchical Semantic Segmentation,
CVPR22(1236-1247)
IEEE DOI
2210
Training, Visualization, Shape, Lips, Semantics, Network architecture,
Segmentation, grouping and shape analysis
BibRef
Coupeau, P.,
Fasquel, J.B.[Jean-Baptiste],
Dinomais, M.,
On the relevance of edge-conditioned convolution for GNN-based
semantic image segmentation using spatial relationships,
IPTA22(1-6)
IEEE DOI
2206
Training, Deep learning, Image segmentation, Convolution,
Image edge detection, Semantics, Convolutional neural networks,
edge-conditioned convolution
BibRef
Chopin, J.[Jérémy],
Fasquel, J.B.[Jean-Baptiste],
Mouchère, H.[Harold],
Dahyot, R.[Rozenn],
Bloch, I.[Isabelle],
Semantic image segmentation based on spatial relationships and
inexact graph matching,
IPTA20(1-6)
IEEE DOI
2206
Training, Image segmentation, Neural networks, Semantics, Tools,
Proposals, Faces, Deep learning, Inexact graph matching, Quadratic assignment problem
BibRef
Liu, M.Y.[Meng-Yu],
Yin, H.J.[Hu-Jun],
Sparse Spatial Attention Network for Semantic Segmentation,
ICIP21(644-648)
IEEE DOI
2201
Image segmentation, Aggregates, Semantics, Neural networks,
Spatial databases, Sparse matrices, Semantic segmentation,
convolutional neural network
BibRef
Michieli, U.[Umberto],
Zanuttigh, P.[Pietro],
Continual Semantic Segmentation via Repulsion-Attraction of Sparse
and Disentangled Latent Representations,
CVPR21(1114-1124)
IEEE DOI
2111
Deep learning, Limiting, Shape, Semantics, Prototypes, Training data
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,
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,
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
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, 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
Borse, S.[Shubhankar],
Klingner, M.[Marvin],
Kumar, V.R.[Varun Ravi],
Cai, H.[Hong],
Almuzairee, A.[Abdulaziz],
Yogamani, S.[Senthil],
Porikli, F.M.[Fatih M.],
X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View
Segmentation,
WACV23(3286-3296)
IEEE DOI
2302
Laser radar, Fuses, Roads, Benchmark testing, Cameras, Reliability,
Applications: Robotics, Virtual/augmented reality
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 .