8.6.3.4 Weakly Supervised, Self Supervised Semantic Segmentation

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Semantic Segmentation. Weakly Supervised. Self-Supervised.

Li, Y.[Yi], Guo, Y.Q.[Yan-Qing], Kao, Y.Y.[Yue-Ying], He, R.[Ran],
Image Piece Learning for Weakly Supervised Semantic Segmentation,
SMCS(47), No. 4, April 2017, pp. 648-659.
IEEE DOI 1704
Correlation BibRef

Shimoda, W.[Wataru], Yanai, K.[Keiji],
Weakly supervised semantic segmentation using distinct class specific saliency maps,
CVIU(191), 2020, pp. 102712.
Elsevier DOI 2002
BibRef
Earlier:
Distinct Class-Specific Saliency Maps for Weakly Supervised Semantic Segmentation,
ECCV16(IV: 218-234).
Springer DOI 1611
Semantic segmentation, Weakly supervised learning, Weakly supervised segmentation, Visualization, Deep learning BibRef

Shimoda, W.[Wataru], Yanai, K.[Keiji],
Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentation,
ICCV19(5207-5216)
IEEE DOI 2004
estimation theory, image denoising, image segmentation, iterative methods, learning (artificial intelligence), Predictive models BibRef

Chen, Y.C.[Yun-Chun], Lin, Y.Y.[Yen-Yu], Yang, M.H.[Ming-Hsuan], Huang, J.B.[Jia-Bin],
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic Matching and Object Co-Segmentation,
PAMI(43), No. 10, October 2021, pp. 3632-3647.
IEEE DOI 2109
Semantics, Task analysis, Image segmentation, Training, Clutter, Proposals, Pattern matching, Semantic matching, weakly-supervised learning BibRef

Zhou, T.F.[Tian-Fei], Li, L.L.[Liu-Lei], Li, X.Y.[Xue-Yi], Feng, C.M.[Chun-Mei], Li, J.W.[Jian-Wu], Shao, L.[Ling],
Group-Wise Learning for Weakly Supervised Semantic Segmentation,
IP(31), 2022, pp. 799-811.
IEEE DOI 2201
Semantics, Image segmentation, Training, Location awareness, Cognition, Task analysis, Graph neural networks, neural attention BibRef

Chen, H.J.[Hong-Jun], Wang, J.B.[Jin-Bao], Chen, H.C.[Hong Cai], Zhen, X.T.[Xian-Tong], Zheng, F.[Feng], Ji, R.R.[Rong-Rong], Shao, L.[Ling],
Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation,
ICCV21(6900-6909)
IEEE DOI 2203
Seminars, Training, Knowledge engineering, Bridges, Costs, Annotations, Segmentation, grouping and shape, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Zhang, R.M.[Rui-Mao], Lin, L.[Liang], Wang, G.R.[Guang-Run], Wang, M.[Meng], Zuo, W.M.[Wang-Meng],
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions,
PAMI(41), No. 3, March 2019, pp. 596-610.
IEEE DOI 1902
Semantics, Labeling, Training, Neural networks, Task analysis, Predictive models, Image segmentation, Scene parsing, recursive structured prediction BibRef

Lin, L.[Liang], Wang, G.R.[Guang-Run], Zhang, R.[Rui], Zhang, R.M.[Rui-Mao], Liang, X.D.[Xiao-Dan], Zuo, W.M.[Wang-Meng],
Deep Structured Scene Parsing by Learning with Image Descriptions,
CVPR16(2276-2284)
IEEE DOI 1612
BibRef

Wang, X.[Xiang], Liu, S.F.[Si-Fei], Ma, H.M.[Hui-Min], Yang, M.H.[Ming-Hsuan],
Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning,
IJCV(128), No. 6, June 2020, pp. 1736-1749.
Springer DOI 2006
BibRef

Chan, L.[Lyndon], Hosseini, M.S.[Mahdi S.], Plataniotis, K.N.[Konstantinos N.],
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains,
IJCV(129), No. 2, February 2021, pp. 361-384.
Springer DOI 2102
BibRef

Krešo, I.[Ivan], Krapac, J.[Josip], Šegvic, S.[Siniša],
Efficient Ladder-Style DenseNets for Semantic Segmentation of Large Images,
ITS(22), No. 8, August 2021, pp. 4951-4961.
IEEE DOI 2108
BibRef
Earlier: A2, A1, A3:
Ladder-Style DenseNets for Semantic Segmentation of Large Natural Images,
CVRoads17(238-245)
IEEE DOI 1802
BibRef
Earlier: A2, A3, Only:
Weakly-Supervised Semantic Segmentation by Redistributing Region Scores Back to the Pixels,
GCPR16(377-388).
Springer DOI 1611
Semantics, Feature extraction, Image segmentation, Computational modeling, Spatial resolution, Checkpointing, road transportation. Convolution, Tensile stress, Training BibRef

Krešo, I.[Ivan], Cauševic, D.[Denis], Krapac, J.[Josip], Šegvic, S.[Siniša],
Convolutional Scale Invariance for Semantic Segmentation,
GCPR16(64-75).
Springer DOI 1611
BibRef

Liang, C.B.[Chen-Bin], Cheng, B.[Bo], Xiao, B.H.[Bai-Hua], He, C.Q.[Chenlin-Qiu], Liu, X.[Xunan], Jia, N.[Ning], Chen, J.[Jinfen],
Semi-/Weakly-Supervised Semantic Segmentation Method and Its Application for Coastal Aquaculture Areas Based on Multi-Source Remote Sensing Images: Taking the Fujian Coastal Area (Mainly Sanduo) as an Example,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Huang, Z.[Zhou], Xiang, T.Z.[Tian-Zhu], Chen, H.X.[Huai-Xin], Dai, H.[Hang],
Scribble-based boundary-aware network for weakly supervised salient object detection in remote sensing images,
PandRS(191), 2022, pp. 290-301.
Elsevier DOI 2208
Salient object detection, Saliency detection, Scribble annotation, Weakly supervised, Remote sensing dataset BibRef

Zhang, B.F.[Bing-Feng], Xiao, J.[Jimin], Wei, Y.C.[Yun-Chao], Huang, K.[Kaizhu], Luo, S.[Shan], Zhao, Y.[Yao],
End-to-end weakly supervised semantic segmentation with reliable region mining,
PR(128), 2022, pp. 108663.
Elsevier DOI 2205
Weakly supervised, Semantic segmentation, End-to-end, Attention BibRef

Jiang, P.T.[Peng-Tao], Han, L.H.[Ling-Hao], Hou, Q.B.[Qi-Bin], Cheng, M.M.[Ming-Ming], Wei, Y.C.[Yun-Chao],
Online Attention Accumulation for Weakly Supervised Semantic Segmentation,
PAMI(44), No. 10, October 2022, pp. 7062-7077.
IEEE DOI 2209
Training, Semantics, Cats, Image segmentation, Visualization, Task analysis, Location awareness, pixel-level supervision BibRef

Zhang, B.F.[Bing-Feng], Xiao, J.[Jimin], Jiao, J.B.[Jian-Bo], Wei, Y.C.[Yun-Chao], Zhao, Y.[Yao],
Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation,
PAMI(44), No. 11, November 2022, pp. 8082-8096.
IEEE DOI 2210
Semantics, Task analysis, Image edge detection, Image segmentation, Reliability, Proposals, Graph neural networks, Weakly supervised, graph neural network BibRef

Hu, P.[Ping], Sclaroff, S.[Stan], Saenko, K.[Kate],
Leveraging Geometric Structure for Label-Efficient Semi-Supervised Scene Segmentation,
IP(31), 2022, pp. 6320-6330.
IEEE DOI 2210
Annotations, Training, Labeling, Solid modeling, Task analysis, Semantics, Semantic segmentation, geometric structure BibRef

Xia, R.Y.[Rui-Yang], Li, G.Q.[Guo-Quan], Huang, Z.W.[Zheng-Wen], Meng, H.Y.[Hong-Ying], Pang, Y.[Yu],
CBASH: Combined Backbone and Advanced Selection Heads With Object Semantic Proposals for Weakly Supervised Object Detection,
CirSysVideo(32), No. 10, October 2022, pp. 6502-6514.
IEEE DOI 2210
Proposals, Semantics, Feature extraction, Object detection, Training, Location awareness, Head, Weakly supervised object detection, object semantic proposals BibRef

Zhou, H.J.[Hua-Jun], Yang, L.X.[Ling-Xiao], Xie, X.H.[Xiao-Hua], Lai, J.H.[Jian-Huang],
Selective Intra-Image Similarity for Personalized Fixation-Based Object Segmentation,
CirSysVideo(32), No. 11, November 2022, pp. 7910-7923.
IEEE DOI 2211
Image segmentation, Task analysis, Object segmentation, Observers, Image edge detection, Object detection, Measurement, evaluation metric BibRef

Chen, Q.[Qi], Yang, L.X.[Ling-Xiao], Lai, J.H.[Jian-Huang], Xie, X.H.[Xiao-Hua],
Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation,
CVPR22(4278-4288)
IEEE DOI 2210
Location awareness, Weight measurement, Image segmentation, Costs, Shape, Semantics, Prototypes, Segmentation, Self- semi- meta- unsupervised learning BibRef

Fasana, C.[Corrado], Pasini, S.[Samuele], Milani, F.[Federico], Fraternali, P.[Piero],
Weakly Supervised Object Detection for Remote Sensing Images: A Survey,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Li, Y.J.[Yu-Jie], Sun, J.X.[Jia-Xing], Li, Y.[Yun],
Weakly-Supervised Semantic Segmentation Network With Iterative dCRF,
ITS(23), No. 12, December 2022, pp. 25419-25426.
IEEE DOI 2212
Semantics, Cams, Image segmentation, Convolution, Annotations, Feature extraction, Training, Weakly-supervised, image-level annotations BibRef

Cheng, L.[Lin], Fang, P.F.[Peng-Fei], Yan, Y.[Yan], Lu, Y.[Yang], Wang, H.Z.[Han-Zi],
TRL: Transformer based refinement learning for hybrid-supervised semantic segmentation,
PRL(164), 2022, pp. 239-245.
Elsevier DOI 2212
Hybrid-supervised semantic segmentation, Simi-supervised semantic segmentation, Pseudo label BibRef

Feng, J.[Jiapei], Wang, X.G.[Xing-Gang], Li, T.[Te], Ji, S.S.[Shan-Shan], Liu, W.Y.[Wen-Yu],
Weakly-supervised semantic segmentation via online pseudo-mask correcting,
PRL(165), 2023, pp. 33-38.
Elsevier DOI 2301
Weakly-supervised learning, Semantic segmentation, Noisy label learning BibRef

Cao, C.[Cong], Lin, T.W.[Tian-Wei], He, D.L.[Dong-Liang], Li, F.[Fu], Yue, H.J.[Huan-Jing], Yang, J.Y.[Jing-Yu], Ding, E.[Errui],
Adversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic Segmentation,
CirSysVideo(33), No. 2, February 2023, pp. 793-803.
IEEE DOI 2302
Semantics, Image segmentation, Training, Perturbation methods, Data models, Context modeling, Task analysis, differentiable spatial warping BibRef

Kim, S.[Soopil], Chikontwe, P.[Philip], An, S.[Sion], Park, S.H.[Sang Hyun],
Uncertainty-aware semi-supervised few shot segmentation,
PR(137), 2023, pp. 109292.
Elsevier DOI 2302
Few shot segmentation, Meta learning, Uncertainty estimation, Semi-supervised learning, Prototype BibRef

Min, H.[Hai], Zhang, Y.[Yemao], Zhao, Y.[Yang], Jia, W.[Wei], Lei, Y.K.[Ying-Ke], Fan, C.X.[Chun-Xiao],
Hybrid feature enhancement network for few-shot semantic segmentation,
PR(137), 2023, pp. 109291.
Elsevier DOI 2302
Semantic segmentation, Few-shot segmentation, Few-shot learning BibRef

Yuan, K.[Kunhao], Schaefer, G.[Gerald], Lai, Y.K.[Yu-Kun], Wang, Y.[Yifan], Liu, X.[Xiyao], Guan, L.[Lin], Fang, H.[Hui],
A multi-strategy contrastive learning framework for weakly supervised semantic segmentation,
PR(137), 2023, pp. 109298.
Elsevier DOI 2302
Weakly supervised learning, Representation learning, Contrastive learning, Semantic segmentation BibRef

Zang, Y.H.[Yu-Hang], Zhou, K.Y.[Kai-Yang], Huang, C.[Chen], Loy, C.C.[Chen Change],
Semi-Supervised and Long-Tailed Object Detection with CascadeMatch,
IJCV(131), No. 1, January 2023, pp. Pages987-1001.
Springer DOI 2303
BibRef

Chen, J.[Jingkun], Zhang, J.G.[Jian-Guo], Debattista, K.[Kurt], Han, J.G.[Jun-Gong],
Semi-Supervised Unpaired Medical Image Segmentation Through Task-Affinity Consistency,
MedImg(42), No. 3, March 2023, pp. 594-605.
IEEE DOI 2303
Image segmentation, Task analysis, Feature extraction, Training, Semisupervised learning, Medical diagnostic imaging, consistency BibRef

Li, R.[Ruiwen], Mai, Z.[Zheda], Zhang, Z.B.[Zhi-Bo], Jang, J.[Jongseong], Sanner, S.[Scott],
TransCAM: Transformer attention-based CAM refinement for Weakly supervised semantic segmentation,
JVCIR(92), 2023, pp. 103800.
Elsevier DOI 2303
Weakly supervised learning, Semantic segmentation, Vision transformer BibRef


Akiva, P.[Peri], Dana, K.[Kristin],
Single Stage Weakly Supervised Semantic Segmentation of Complex Scenes,
WACV23(5943-5954)
IEEE DOI 2302
Training, Annotations, Semantic segmentation, Semantics, Focusing, Benchmark testing, Applications: Agriculture, Remote Sensing BibRef

Rangnekar, A.[Aneesh], Kanan, C.[Christopher], Hoffman, M.[Matthew],
Semantic Segmentation with Active Semi-Supervised Learning,
WACV23(5955-5966)
IEEE DOI 2302
Training, Deep learning, Head, Costs, Annotations, Semantic segmentation, Training data, visual reasoning BibRef

Kong, H.[Heejo], Lee, G.H.[Gun-Hee], Kim, S.[Suneung], Lee, S.W.[Seong-Whan],
Pruning-Guided Curriculum Learning for Semi-Supervised Semantic Segmentation,
WACV23(5903-5912)
IEEE DOI 2302
Training, Knowledge engineering, Semantic segmentation, Benchmark testing, Feature extraction, Noise measurement BibRef

Cheng, W.L.[Wen-Li], Jiao, J.J.[Jia-Jia],
CAU: A Consensus Model of Augmented Unlabeled Data for Medical Image Segmentation,
ICIVC22(368-374)
IEEE DOI 2301
Training, Image segmentation, Machine learning algorithms, Semisupervised learning, Prediction algorithms, Data models, Data Augmentation BibRef

Chai, L.[Lu], Liu, Q.[Qinyuan],
Semi-Supervised Semantic Segmentation of Class-Imbalanced Images: A Hierarchical Self-Attention Generative Adversarial Network,
ICIVC22(398-404)
IEEE DOI 2301
Image segmentation, Image synthesis, Computational modeling, Biological system modeling, Semantics, Pipelines, biomedical images BibRef

Teh, E.W.[Eu Wern], de Vries, T.[Terrance], Duke, B.[Brendan], Jiang, R.[Ruowei], Aarabi, P.[Parham], Taylor, G.W.[Graham W.],
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation,
CRV22(58-66)
IEEE DOI 2301
Greedy Iterative Self-Training (GIST) and Random Iterative Self-Training (RIST). Training, Degradation, Semantics, Semisupervised learning, Behavioral sciences, Iterative methods, Task analysis, self-training BibRef

Pauletto, L.[Loďc], Amini, M.R.[Massih-Reza], Winckler, N.[Nicolas],
Se2NAS: Self-Semi-Supervised architecture optimization for Semantic Segmentation,
ICPR22(54-60)
IEEE DOI 2212
Semantic segmentation, Training data, Artificial neural networks, Self-supervised learning, Semisupervised learning, Routing BibRef

Chen, Y.[Ying], Ouyang, X.[Xu], Zhu, K.Y.[Kai-Yue], Agam, G.[Gady],
Semi-supervised Dual-Domain Adaptation for Semantic Segmentation,
ICPR22(230-237)
IEEE DOI 2212
Deep learning, Annotations, Semantic segmentation, Supervised learning, Semisupervised learning, Benchmark testing BibRef

Fan, Z.K.[Zhen-Kun], Sun, X.[Xin], Dong, J.Y.[Jun-Yu],
Average Activation Network for Weakly Supervised Semantic Segmentation,
ICPR22(657-662)
IEEE DOI 2212
Training, Limiting, Semantic segmentation, Pattern recognition BibRef

Wang, L.T.[Lu-Ting], Li, X.J.[Xiao-Jie], Liao, Y.[Yue], Jiang, Z.[Zeren], Wu, J.L.[Jian-Long], Wang, F.[Fei], Qian, C.[Chen], Liu, S.[Si],
HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors,
ECCV22(IX:314-331).
Springer DOI 2211
BibRef

Chen, Z.Z.[Zhao-Zheng], Wang, T.[Tan], Wu, X.[Xiongwei], Hua, X.S.[Xian-Sheng], Zhang, H.[Hanwang], Sun, Q.[Qianru],
Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation,
CVPR22(959-968)
IEEE DOI 2210
Codes, Shape, Computational modeling, Semantics, Benchmark testing, Feature extraction, Recognition: detection, categorization, grouping and shape analysis BibRef

Xie, J.[Jinheng], Hou, X.[Xianxu], Ye, K.[Kai], Shen, L.L.[Lin-Lin],
CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation,
CVPR22(4473-4482)
IEEE DOI 2210
Image segmentation, Codes, Shape, Image matching, Semantics, Natural languages, Segmentation, grouping and shape analysis, Vision + language BibRef

Chen, Z.[Zhang], Tian, Z.Q.[Zhi-Qiang], Zhu, J.[Jihua], Li, C.[Ce], Du, S.[Shaoyi],
C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image,
CVPR22(11666-11675)
IEEE DOI 2210
Training, Chaos, Image segmentation, Codes, Semantics, Anatomical structure, Segmentation, grouping and shape analysis BibRef

Li, J.[Jing], Fan, J.S.[Jun-Song], Zhang, Z.X.[Zhao-Xiang],
Towards Noiseless Object Contours for Weakly Supervised Semantic Segmentation,
CVPR22(16835-16844)
IEEE DOI 2210
Training, Location awareness, Image segmentation, Semantics, Refining, Predictive models, Scene analysis and understanding, grouping and shape analysis BibRef

Zhou, T.[Tianfei], Zhang, M.[Meijie], Zhao, F.[Fang], Li, J.[Jianwu],
Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation,
CVPR22(4289-4299)
IEEE DOI 2210
Training, Location awareness, Annotations, Semantics, Training data, Benchmark testing, Segmentation, grouping and shape analysis, Scene analysis and understanding BibRef

Du, Y.[Ye], Fu, Z.[Zehua], Liu, Q.J.[Qing-Jie], Wang, Y.H.[Yun-Hong],
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast,
CVPR22(4310-4319)
IEEE DOI 2210
Image segmentation, Shape, Semantics, Prototypes, Estimation, Pattern recognition, Segmentation, grouping and shape analysis BibRef

Xie, J.H.[Jin-Heng], Xiang, J.F.[Jian-Feng], Chen, J.L.[Jun-Liang], Hou, X.X.[Xian-Xu], Zhao, X.D.[Xiao-Dong], Shen, L.L.[Lin-Lin],
C2 AM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation,
CVPR22(979-988)
IEEE DOI 2210
Location awareness, Image segmentation, Codes, Shape, Semantics, Force, Recognition: detection, categorization, retrieval, Segmentation, Self- semi- meta- unsupervised learning BibRef

Sun, W.X.[Wei-Xuan], Zhang, J.[Jing], Barnes, N.[Nick],
Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation,
WACV22(2653-2662)
IEEE DOI 2202
Training, Image segmentation, Semantics, Pipelines, Inference algorithms, Cams, Segmentation, Grouping and Shape BibRef

Wu, T.[Tong], Huang, J.[Junshi], Gao, G.[Guangyu], Wei, X.M.[Xiao-Ming], Wei, X.L.[Xiao-Lin], Luo, X.[Xuan], Liu, C.H.[Chi Harold],
Embedded Discriminative Attention Mechanism for Weakly Supervised Semantic Segmentation,
CVPR21(16760-16769)
IEEE DOI 2111
Image segmentation, Codes, Annotations, Semantics, Collaboration, Feature extraction BibRef

Lee, S.[Seungho], Lee, M.[Minhyun], Lee, J.[Jongwuk], Shim, H.J.[Hyun-Jung],
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for Weakly Supervised Semantic Segmentation,
CVPR21(5491-5501)
IEEE DOI 2111
Location awareness, Training, Image segmentation, Codes, Semantics, Pattern recognition BibRef

Oh, Y.[Youngmin], Kim, B.[Beomjun], Ham, B.[Bumsub],
Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation,
CVPR21(6909-6918)
IEEE DOI 2111
Training, Image segmentation, Annotations, Computational modeling, Semantics, Feature extraction BibRef

Liu, Z.Z.[Zheng-Zhe], Qi, X.J.[Xiao-Juan], Fu, C.W.[Chi-Wing],
One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation,
CVPR21(1726-1736)
IEEE DOI 2111
Training, Annotations, Semantics, Training data, Prototypes BibRef

Neven, R.[Robby], Neven, D.[Davy], de Brabandere, B.[Bert], Proesmans, M.[Marc], Goedemé, T.[Toon],
Weakly-Supervised Semantic Segmentation by Learning Label Uncertainty,
ILDAV21(1678-1686)
IEEE DOI 2112
Training, Deep learning, Image segmentation, Uncertainty, Semantics, Neural networks BibRef

Yin, J.H.[Jun-Hui], Zhang, S.Q.[Si-Qing], Chang, D.L.[Dong-Liang], Ma, Z.Y.[Zhan-Yu], Guo, J.[Jun],
Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization,
ICPR21(4229-4236)
IEEE DOI 2105
weakly supervised object localization. Location awareness, Training, Deep learning, Adaptation models, Visualization, Automobiles BibRef

Cao, T.Y.[Tian-Yue], Du, L.Y.[Lian-Yu], Zhang, X.Y.[Xiao-Yun], Chen, S.H.[Si-Heng], Zhang, Y.[Ya], Wang, Y.F.[Yan-Feng],
CaT: Weakly Supervised Object Detection with Category Transfer,
ICCV21(3050-3059)
IEEE DOI 2203
Convolutional codes, Bridges, Semantics, Object detection, Detectors, Knowledge transfer, Detection and localization in 2D and 3D, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Yamazaki, M.[Masaki], Peng, X.C.[Xing-Chao], Saito, K.[Kuniaki], Hu, P.[Ping], Saenko, K.[Kate], Taniguchi, Y.[Yasuhiro],
Weakly Supervised Domain Adaptation using Super-pixel labeling for Semantic Segmentation,
MVA21(1-5)
DOI Link 2109
Deep learning, Image segmentation, Adaptation models, Annotations, Semantics, Object segmentation, Data models BibRef

Watanabe, K.[Kohei], Saito, K.[Kuniaki], Ushiku, Y.[Yoshitaka], Harada, T.[Tatsuya],
Multichannel Semantic Segmentation with Unsupervised Domain Adaptation,
AutoNUE18(V:600-616).
Springer DOI 1905
BibRef

Zhang, T.Y.[Tian-Yi], Lin, G.S.[Guo-Sheng], Liu, W.D.[Wei-De], Cai, J.F.[Jian-Fei], Kot, A.[Alex],
Splitting Vs. Merging: Mining Object Regions with Discrepancy and Intersection Loss for Weakly Supervised Semantic Segmentation,
ECCV20(XXII:663-679).
Springer DOI 2011
BibRef

Fan, J.S.[Jun-Song], Zhang, Z.X.[Zhao-Xiang], Tan, T.N.[Tie-Niu],
Employing Multi-estimations for Weakly-supervised Semantic Segmentation,
ECCV20(XVII:332-348).
Springer DOI 2011
BibRef

Chang, Y., Wang, Q., Hung, W., Piramuthu, R., Tsai, Y., Yang, M.,
Weakly-Supervised Semantic Segmentation via Sub-Category Exploration,
CVPR20(8988-8997)
IEEE DOI 2008
Task analysis, Feature extraction, Semantics, Training, Image segmentation, Computational modeling BibRef

Fan, J., Zhang, Z., Song, C., Tan, T.,
Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation,
CVPR20(4282-4291)
IEEE DOI 2008
Image segmentation, Semantics, Training, Task analysis, Manifolds, Estimation, Benchmark testing BibRef

Chen, L.[Liyi], Wu, W.W.[Wei-Wei], Fu, C.C.[Chen-Chen], Han, X.[Xiao], Zhang, Y.T.[Yun-Tao],
Weakly Supervised Semantic Segmentation with Boundary Exploration,
ECCV20(XXVI:347-362).
Springer DOI 2011
BibRef

Sun, G.L.[Guo-Lei], Wang, W.G.[Wen-Guan], Dai, J.F.[Ji-Feng], Van Gool, L.J.[Luc J.],
Mining Cross-image Semantics for Weakly Supervised Semantic Segmentation,
ECCV20(II:347-365).
Springer DOI 2011
BibRef

Zareian, A., Karaman, S., Chang, S.,
Weakly Supervised Visual Semantic Parsing,
CVPR20(3733-3742)
IEEE DOI 2008
Semantics, Visualization, Proposals, Image edge detection, Message passing, Task analysis BibRef

Yu, Z., Zhuge, Y., Lu, H., Zhang, L.,
Joint Learning of Saliency Detection and Weakly Supervised Semantic Segmentation,
ICCV19(7222-7232)
IEEE DOI 2004
image classification, image coding, image recognition, image segmentation, object detection, supervised learning, WSSS, Computational modeling BibRef

Marin, D.[Dmitrii], Boykov, Y.Y.[Yuri Y.],
Robust Trust Region for Weakly Supervised Segmentation,
ICCV21(6588-6598)
IEEE DOI 2203
Training, Deep learning, Image segmentation, Semantics, Neural networks, Training data, Vision applications and systems BibRef

Shen, Y.H.[Yun-Hang], Cao, L.J.[Liu-Juan], Chen, Z.W.[Zhi-Wei], Zhang, B.C.[Bao-Chang], Su, C.[Chi], Wu, Y.J.[Yong-Jian], Huang, F.Y.[Fei-Yue], Ji, R.R.[Rong-Rong],
Parallel Detection-and-Segmentation Learning for Weakly Supervised Instance Segmentation,
ICCV21(8178-8188)
IEEE DOI 2203
Training, Image segmentation, Correlation, Pipelines, Coherence, Object detection, Transfer/Low-shot/Semi/Unsupervised Learning, grouping and shape BibRef

Shen, Y.H.[Yun-Hang], Ji, R.R.[Rong-Rong], Wang, Y.[Yan], Wu, Y.J.[Yong-Jian], Cao, L.J.[Liu-Juan],
Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation,
CVPR19(697-707).
IEEE DOI 2002
BibRef

Xu, L.[Lian], Ouyang, W.L.[Wan-Li], Bennamoun, M.[Mohammed], Boussaid, F.[Farid], Xu, D.[Dan],
Multi-class Token Transformer for Weakly Supervised Semantic Segmentation,
CVPR22(4300-4309)
IEEE DOI 2210
Location awareness, Shape, Semantics, Object detection, Transformers, Pattern recognition, Segmentation, grouping and shape analysis BibRef

Song, C.F.[Chun-Feng], Huang, Y.[Yan], Ouyang, W.L.[Wan-Li], Wang, L.[Liang],
Box-Driven Class-Wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation,
CVPR19(3131-3140).
IEEE DOI 2002
BibRef

Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S.,
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation,
CVPR18(7268-7277)
IEEE DOI 1812
Image segmentation, Semantics, Convolution, Training, Kernel, Standards, Head BibRef

Vernaza, P., Chandraker, M.,
Learning Random-Walk Label Propagation for Weakly-Supervised Semantic Segmentation,
CVPR17(2953-2961)
IEEE DOI 1711
Image edge detection, Image segmentation, Labeling, Semantics, Training, Uncertainty BibRef

Xing, F.Z., Cambria, E., Huang, W.B., Xu, Y.,
Weakly supervised semantic segmentation with superpixel embedding,
ICIP16(1269-1273)
IEEE DOI 1610
Context BibRef

Pourian, N., Karthikeyan, S., Manjunath, B.S.,
Weakly Supervised Graph Based Semantic Segmentation by Learning Communities of Image-Parts,
ICCV15(1359-1367)
IEEE DOI 1602
Correlation BibRef

Zhang, W.[Wei], Zeng, S.[Sheng], Wang, D.[Dequan], Xue, X.Y.[Xiang-Yang],
Weakly supervised semantic segmentation for social images,
CVPR15(2718-2726)
IEEE DOI 1510
BibRef

Ying, P.[Peng], Liu, J.[Jing], Lu, H.Q.[Han-Qing],
Dictionary learning based superpixels clustering for weakly-supervised semantic segmentation,
ICIP15(4258-4262)
IEEE DOI 1512
Weak supervision;dictionary learning;semantic segmentation BibRef

Liu, Y.[Yang], Liu, J.[Jing], Li, Z.[Zechao], Tang, J.H.[Jin-Hui], Lu, H.Q.[Han-Qing],
Weakly-Supervised Dual Clustering for Image Semantic Segmentation,
CVPR13(2075-2082)
IEEE DOI 1309
Image Semantic Segmentation; Weakly-Supervised BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Fua and Leclerc Guided Segmentation Papers .


Last update:Mar 27, 2023 at 09:32:08