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
Jiang, P.T.[Peng-Tao],
Yang, Y.Q.[Yu-Qi],
Hou, Q.B.[Qi-Bin],
Wei, Y.C.[Yun-Chao],
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly
Supervised Semantic Segmentation,
CVPR22(16865-16875)
IEEE DOI
2210
Knowledge engineering, Image segmentation, Codes, Shape, Annotations,
Semantics, Scene analysis and understanding, Segmentation, grouping and shape analysis
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
Zhu, X.O.[Xia-Obin],
Zhang, X.M.[Xin-Ming],
Zhang, X.Y.[Xiao-Yu],
Xue, Z.Y.[Zi-Yu],
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
di Mauro, D.[Daniele],
Furnari, A.[Antonino],
Patanè, G.[Giuseppe],
Battiato, S.[Sebastiano],
Farinella, G.M.[Giovanni Maria],
SceneAdapt: Scene-based domain adaptation for semantic segmentation
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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
Azadi, S.[Samaneh],
Pathak, D.[Deepak],
Ebrahimi, S.[Sayna],
Darrell, T.J.[Trevor J.],
Compositional GAN: Learning Image-Conditional Binary Composition,
IJCV(128), No. 10-11, November 2020, pp. 2570-2585.
Springer DOI
2009
BibRef
Azadi, S.[Samaneh],
Feng, J.S.[Jia-Shi],
Darrell, T.J.[Trevor J.],
Learning Detection with Diverse Proposals,
CVPR17(7369-7377)
IEEE DOI
1711
Convolutional codes, Kernel,
Object detection, Proposals, Semantics, Training
BibRef
Prakash, C.D.[Charan D.],
Karam, L.J.[Lina J.],
It GAN Do Better: GAN-Based Detection of Objects on Images With
Varying Quality,
IP(30), 2021, pp. 9220-9230.
IEEE DOI
2112
Object detection, Training, Image quality, Computational modeling,
Task analysis, Generators, Distortion, Object detection, GAN,
RetinaNet
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
Wang, Y.D.[Yu-De],
Zhang, J.[Jie],
Kan, M.[Meina],
Shan, S.G.[Shi-Guang],
Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic
Segmentation,
PR(132), 2022, pp. 108925.
Elsevier DOI
2209
Semi-supervised, Weakly supervised, Semi-and-weakly supervised,
Semantic segmentation, Pseudo label, Self-training
BibRef
Cai, X.[Xin],
Zeng, J.B.[Jia-Bei],
Shan, S.G.[Shi-Guang],
Landmark-aware Self-supervised Eye Semantic Segmentation,
FG21(1-8)
IEEE DOI
2303
Training, Image segmentation, Shape, Face recognition, Semantics,
Training data, Gesture recognition
BibRef
Wang, Y.[Yude],
Zhang, J.[Jie],
Kan, M.[Meina],
Shan, S.G.[Shi-Guang],
Chen, X.L.[Xi-Lin],
Self-Supervised Equivariant Attention Mechanism for Weakly Supervised
Semantic Segmentation,
CVPR20(12272-12281)
IEEE DOI
2008
Image segmentation, Semantics, Phase change materials,
Task analysis, Correlation, Aggregates, Supervised learning
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.R.[Chao-Ran],
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
Kho, S.[Sungpil],
Lee, P.[Pilhyeon],
Lee, W.[Wonyoung],
Ki, M.[Minsong],
Byun, H.R.[Hye-Ran],
Exploiting shape cues for weakly supervised semantic segmentation,
PR(132), 2022, pp. 108953.
Elsevier DOI
2209
Semantic segmentation, Weakly supervised learning,
Texture biases, Shape cues
BibRef
Jaritz, M.[Maximilian],
Vu, T.H.[Tuan-Hung],
de Charette, R.[Raoul],
Wirbel, É.[Émilie],
Pérez, P.[Patrick],
Cross-Modal Learning for Domain Adaptation in 3D Semantic
Segmentation,
PAMI(45), No. 2, February 2023, pp. 1533-1544.
IEEE DOI
2301
BibRef
Earlier:
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic
Segmentation,
CVPR20(12602-12611)
IEEE DOI
2008
Image segmentation, Point cloud compression, Laser radar,
Semantics, Task analysis, Robot sensing systems, Domain adaptation, 2D/3D.
Training
BibRef
Lee, S.H.[Seong-Ho],
Bae, S.H.[Seung-Hwan],
AFI-GAN: Improving feature interpolation of feature pyramid networks
via adversarial training for object detection,
PR(138), 2023, pp. 109365.
Elsevier DOI
2303
Multi-scale feature representation, Object detection,
Adversarial training, Feature up-sampling
BibRef
Bae, S.H.[Seung-Hwan],
Deformable Part Region Learning and Feature Aggregation Tree
Representation for Object Detection,
PAMI(45), No. 9, September 2023, pp. 10817-10834.
IEEE DOI
2309
BibRef
Zhang, Y.H.[Yi-Heng],
Yao, T.[Ting],
Qiu, Z.F.[Zhao-Fan],
Mei, T.[Tao],
Lightweight and Progressively-Scalable Networks for Semantic
Segmentation,
IJCV(131), No. 8, August 2023, pp. 2153-2171.
Springer DOI
2307
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
Rony, J.[Jérôme],
Pesquet, J.C.[Jean-Christophe],
Ayed, I.B.[Ismail Ben],
Proximal Splitting Adversarial Attack for Semantic Segmentation,
CVPR23(20524-20533)
IEEE DOI
2309
BibRef
Chen, J.Q.[Jia-Qi],
Lu, J.C.[Jia-Chen],
Zhu, X.T.[Xia-Tian],
Zhang, L.[Li],
Generative Semantic Segmentation,
CVPR23(7111-7120)
IEEE DOI
2309
BibRef
Workman, S.[Scott],
Hadzic, A.[Armin],
Rafique, M.U.[M. Usman],
Handling Image and Label Resolution Mismatch in Remote Sensing,
WACV23(3698-3707)
IEEE DOI
2302
Image resolution, Annotations, Semantic segmentation,
Adversarial machine learning, Task analysis, Remote sensing, visual reasoning
BibRef
Lim, G.[Gyeongsup],
Kim, M.[Minjae],
Hur, J.[Junbeom],
Adversarial Attack on Semantic Segmentation Preprocessed with Super
Resolution,
ICPR22(484-490)
IEEE DOI
2212
Computational modeling, Semantic segmentation,
Perturbation methods, Superresolution, Noise reduction
BibRef
Dong, Z.[Ziyi],
Wei, P.X.[Peng-Xu],
Lin, L.[Liang],
Adversarially-Aware Robust Object Detector,
ECCV22(IX:297-313).
Springer DOI
2211
BibRef
Kassapis, E.[Elias],
Dikov, G.[Georgi],
Gupta, D.K.[Deepak K.],
Nugteren, C.[Cedric],
Calibrated Adversarial Refinement for Stochastic Semantic
Segmentation,
ICCV21(7037-7047)
IEEE DOI
2203
Uncertainty, Semantics, Toy manufacturing industry,
Stochastic processes, Probabilistic logic, Robustness,
Vision applications and systems
BibRef
Xu, X.G.[Xiao-Gang],
Zhao, H.S.[Heng-Shuang],
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.J.[Shuai-Jun],
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.J.[Shuai-Jun],
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
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],
Douillard, A.[Arthur],
Vu, T.H.[Tuan-Hung],
Pérez, P.[Patrick],
Cord, M.[Matthieu],
Multi-Head Distillation for Continual Unsupervised Domain Adaptation
in Semantic Segmentation,
CLVision22(3750-3759)
IEEE DOI
2210
Training, Adaptation models, Urban areas, Semantics,
Transfer learning, Computer architecture, Benchmark testing
BibRef
Vobecky, A.[Antonin],
Hurych, D.[David],
Siméoni, O.[Oriane],
Gidaris, S.[Spyros],
Bursuc, A.[Andrei],
Pérez, P.[Patrick],
Sivic, J.[Josef],
Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via
Cross-Modal Distillation,
ECCV22(XXXVIII:478-495).
Springer DOI
2211
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
Yang, Y.H.[Yung-Hsu],
Huang, T.E.[Thomas E.],
Sun, M.[Min],
Bulò, S.R.[Samuel Rota],
Kontschieder, P.[Peter],
Yu, F.[Fisher],
Dense Prediction with Attentive Feature Aggregation,
WACV23(97-106)
IEEE DOI
2302
Costs, Fuses, Inference mechanisms, Computational modeling,
Semantic segmentation, Predictive models, segmentation)
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 .