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
Bi, L.[Lei],
Feng, D.[Dagan],
Kim, J.M.[Jin-Man],
Dual-Path Adversarial Learning for Fully Convolutional Network
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VC(34), No. 6-8, June 2018, pp. 1043-1052.
WWW Link.
1806
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
Benjdira, B.[Bilel],
Bazi, Y.[Yakoub],
Koubaa, A.[Anis],
Ouni, K.[Kais],
Unsupervised Domain Adaptation Using Generative Adversarial Networks
for Semantic Segmentation of Aerial Images,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link
1906
BibRef
Wang, Q.,
Gao, J.,
Li, X.,
Weakly Supervised Adversarial Domain Adaptation for Semantic
Segmentation in Urban Scenes,
IP(28), No. 9, Sep. 2019, pp. 4376-4386.
IEEE DOI
1908
convolutional neural nets, feature extraction,
image classification, image segmentation,
weakly supervision
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
di Mauro, D.[Daniele],
Furnari, A.[Antonino],
Patanč, G.[Giuseppe],
Battiato, S.[Sebastiano],
Farinella, G.M.[Giovanni Maria],
SceneAdapt: Scene-based domain adaptation for semantic segmentation
using adversarial learning,
PRL(136), 2020, pp. 175-182.
Elsevier DOI
2008
Semantic segmentation, Domain adaptation, Scene adaptation,
Adversarial learning,
BibRef
Arnab, A.,
Miksik, O.,
Torr, P.H.S.,
On the Robustness of Semantic Segmentation Models to Adversarial
Attacks,
PAMI(42), No. 12, December 2020, pp. 3040-3053.
IEEE DOI
2011
BibRef
Earlier:
CVPR18(888-897)
IEEE DOI
1812
Robustness, Semantics, Image segmentation, Perturbation methods,
Deep learning, Neural networks, Image recognition,
machine learning security.
Task analysis, Training
BibRef
Wang, Y.D.[Yi-Dong],
Mo, L.[Lisha],
Ma, H.M.[Hui-Min],
Yuan, J.[Jian],
OccGAN: Semantic image augmentation for driving scenes,
PRL(136), 2020, pp. 257-263.
Elsevier DOI
2008
Occlusion, GAN, Semantic, Augmentation, Cityscapes
BibRef
Tasar, O.,
Happy, S.L.,
Tarabalka, Y.,
Alliez, P.,
ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation
Using Color Mapping Generative Adversarial Networks,
GeoRS(58), No. 10, October 2020, pp. 7178-7193.
IEEE DOI
2009
Training, Remote sensing, Image segmentation, Training data,
Semantics, Image color analysis, Generative adversarial networks,
semantic segmentation
BibRef
Tasar, O.,
Tarabalka, Y.,
Giros, A.,
Alliez, P.,
Clerc, S.,
StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation
of Very High Resolution Satellite Images by Data Standardization,
EarthVision20(747-756)
IEEE DOI
2008
Urban areas, Generators, Remote sensing, Task analysis, Semantics,
Image segmentation
BibRef
Toldo, M.[Marco],
Michieli, U.[Umberto],
Agresti, G.[Gianluca],
Zanuttigh, P.[Pietro],
Unsupervised domain adaptation for mobile semantic segmentation based
on cycle consistency and feature alignment,
IVC(95), 2020, pp. 103889.
Elsevier DOI
2004
Unsupervised domain adaptation, Semantic segmentation,
Adversarial learning, Transfer learning, Image-to-image translation
BibRef
Huang, W.[Wei],
Shao, Z.F.[Zhan-Fei],
Luo, M.Y.[Ming-Yuan],
Zhang, P.[Peng],
Zha, Y.F.[Yu-Fei],
A novel multi-loss-based deep adversarial network for handling
challenging cases in semi-supervised image semantic segmentation,
PRL(146), 2021, pp. 208-214.
Elsevier DOI
2105
Image semantic segmentation, Semi-supervised learning, Deep adversarial network
BibRef
Mittal, S.[Sudhanshu],
Tatarchenko, M.[Maxim],
Brox, T.[Thomas],
Semi-Supervised Semantic Segmentation With High- and Low-Level
Consistency,
PAMI(43), No. 4, April 2021, pp. 1369-1379.
IEEE DOI
2103
Image segmentation, Training, Semantics, Generators,
Standards, Semisupervised learning,
generative adversarial networks
BibRef
Bai, X.[Xing],
Zhou, J.[Jun],
Parallel global convolutional network for semantic image segmentation,
IET-IPR(15), No. 1, 2021, pp. 252-259.
DOI Link
2106
BibRef
Zhang, J.[Jia],
Li, Z.X.[Zhi-Xin],
Zhang, C.L.[Can-Long],
Ma, H.F.[Hui-Fang],
Stable self-attention adversarial learning for semi-supervised
semantic image segmentation,
JVCIR(78), 2021, pp. 103170.
Elsevier DOI
2107
BibRef
Earlier:
Robust Adversarial Learning For Semi-Supervised Semantic Segmentation,
ICIP20(728-732)
IEEE DOI
2011
Self-Attention Mechanism, Adversarial Learning,
Semi-Supervised Learning, Spectral Normalization, Semantic Image Segmentation.
Training, Semantics, Image segmentation,
Generative adversarial networks, Lips, Generators, Self-Attention,
Spectral Normalization
BibRef
Jamali-Rad, H.[Hadi],
Szabó, A.[Attila],
Lookahead adversarial learning for near real-time semantic
segmentation,
CVIU(212), 2021, pp. 103271.
Elsevier DOI
2110
Semantic segmentation, Conditional adversarial training,
Deep learning
BibRef
Huang, J.X.[Jia-Xing],
Guan, D.[Dayan],
Xiao, A.[Aoran],
Lu, S.J.[Shi-Jian],
Multi-level adversarial network for domain adaptive semantic
segmentation,
PR(123), 2022, pp. 108384.
Elsevier DOI
2112
Unsupervised domain adaptation, Semantic segmentation,
Adversarial learning, Self training
BibRef
Wang, W.[Wen],
Zhang, J.[Jing],
Zhai, W.[Wei],
Cao, Y.[Yang],
Tao, D.C.[Da-Cheng],
Robust Object Detection via Adversarial Novel Style Exploration,
IP(31), 2022, pp. 1949-1962.
IEEE DOI
2202
Degradation, Compounds, Training, Object detection,
Adaptation models, Task analysis, Image enhancement, adversarial learning
BibRef
Luo, Y.[Yawei],
Liu, P.[Ping],
Zheng, L.[Liang],
Guan, T.[Tao],
Yu, J.Q.[Jun-Qing],
Yang, Y.[Yi],
Category-Level Adversarial Adaptation for Semantic Segmentation Using
Purified Features,
PAMI(44), No. 8, August 2022, pp. 3940-3956.
IEEE DOI
2207
Semantics, Task analysis, Training, Loss measurement,
Feature extraction, Visualization, Computer science,
information bottleneck
BibRef
Wang, D.[Derui],
Li, C.[Chaoran],
Wen, S.[Sheng],
Han, Q.L.[Qing-Long],
Nepal, S.[Surya],
Zhang, X.Y.[Xiang-Yu],
Xiang, Y.[Yang],
Daedalus: Breaking Nonmaximum Suppression in Object Detection via
Adversarial Examples,
Cyber(52), No. 8, August 2022, pp. 7427-7440.
IEEE DOI
2208
Task analysis, Perturbation methods, Biological system modeling,
Load modeling, Feature extraction, Computational modeling,
object detection (OD)
BibRef
Xu, X.G.[Xiao-Gang],
Zhao, H.[Hengshuang],
Jia, J.Y.[Jia-Ya],
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic
Segmentation,
ICCV21(7466-7475)
IEEE DOI
2203
Training, Costs, Computational modeling, Perturbation methods,
Semantics, Neural networks, Robustness, Segmentation, Adversarial learning
BibRef
Kim, D.[Dahye],
Hong, B.W.[Byung-Woo],
Unsupervised Segmentation incorporating Shape Prior via Generative
Adversarial Networks,
ICCV21(7304-7314)
IEEE DOI
2203
Deep learning, Image segmentation, Shape, Lighting,
Benchmark testing, Generative adversarial networks, Segmentation,
Neural generative models
BibRef
Kugelman, J.[Jason],
Alonso-Caneiro, D.[David],
Read, S.A.[Scott A.],
Vincent, S.J.[Stephen J.],
Chen, F.K.[Fred K.],
Collins, M.J.[Michael J.],
Dual image and mask synthesis with GANs for semantic segmentation in
optical coherence tomography,
DICTA20(1-8)
IEEE DOI
2201
Training, Deep learning, Image segmentation,
Optical coherence tomography, Semantics, neural networks
BibRef
Li, D.Q.[Dai-Qing],
Yang, J.L.[Jun-Lin],
Kreis, K.[Karsten],
Torralba, A.B.[Antonio B.],
Fidler, S.[Sanja],
Semantic Segmentation with Generative Models: Semi-Supervised
Learning and Strong Out-of-Domain Generalization,
CVPR21(8296-8307)
IEEE DOI
2111
Training, Image segmentation, Annotations, Animals, Face recognition,
Computational modeling, Semantics
BibRef
He, J.Z.[Jian-Zhong],
Jia, X.[Xu],
Chen, S.[Shuaijun],
Liu, J.Z.[Jian-Zhuang],
Multi-Source Domain Adaptation with Collaborative Learning for
Semantic Segmentation,
CVPR21(11003-11012)
IEEE DOI
2111
Training, Image segmentation, Adaptation models,
Semantics, Benchmark testing, Collaborative work
BibRef
Isobe, T.[Takashi],
Jia, X.[Xu],
Chen, S.[Shuaijun],
He, J.Z.[Jian-Zhong],
Shi, Y.J.[Yong-Jie],
Liu, J.Z.[Jian-Zhuang],
Lu, H.C.[Hu-Chuan],
Wang, S.J.[Sheng-Jin],
Multi-Target Domain Adaptation with Collaborative Consistency
Learning,
CVPR21(8183-8192)
IEEE DOI
2111
Adaptation models, Image segmentation,
Computational modeling, Semantics, Collaboration, Predictive models
BibRef
Wang, H.R.[Hao-Ran],
Shen, T.[Tong],
Zhang, W.[Wei],
Duan, L.Y.[Ling-Yu],
Mei, T.[Tao],
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain
Semantic Segmentation,
ECCV20(XIV:642-659).
Springer DOI
2011
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
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
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
Shukla, S.,
Van Gool, L.J.,
Timofte, R.,
Extremely Weak Supervised Image-to-Image Translation for Semantic
Segmentation,
AIM19(3368-3377)
IEEE DOI
2004
image classification, image segmentation, supervised learning,
unsupervised learning, generative models, adversarial training,
semi supervised learning
BibRef
Saporta, A.[Antoine],
Vu, T.H.[Tuan-Hung],
Cord, M.[Matthieu],
Pérez, P.[Patrick],
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic
Segmentation,
ICCV21(9052-9061)
IEEE DOI
2203
Adaptation models, Autonomous systems, Semantics,
Adversarial machine learning, Task analysis, Knowledge transfer,
Vision for robotics and autonomous vehicles
BibRef
Vu, T.H.[Tuan-Hung],
Jain, H.[Himalaya],
Bucher, M.[Maxime],
Cord, M.[Matthieu],
Perez, P.[Patrick],
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in
Semantic Segmentation,
CVPR19(2512-2521).
IEEE DOI
2002
BibRef
Xiao, C.W.[Chao-Wei],
Deng, R.Z.[Rui-Zhi],
Li, B.[Bo],
Yu, F.[Fisher],
Liu, M.Y.[Ming-Yan],
Song, D.[Dawn],
Characterizing Adversarial Examples Based on Spatial Consistency
Information for Semantic Segmentation,
ECCV18(X: 220-237).
Springer DOI
1810
BibRef
Xie, C.,
Wang, J.,
Zhang, Z.,
Zhou, Y.,
Xie, L.,
Yuille, A.L.[Alan L.],
Adversarial Examples for Semantic Segmentation and Object Detection,
ICCV17(1378-1387)
IEEE DOI
1802
image classification, image segmentation, object detection,
pattern clustering, adversarial perturbations,
Semantics
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
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Encoder-Decoder Networks for Semantic Segmentation .