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
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 .