Zhou, Y.,
Huo, S.,
Xiang, W.,
Hou, C.,
Kung, S.Y.,
Semi-Supervised Salient Object Detection Using a Linear Feedback
Control System Model,
Cyber(49), No. 4, April 2019, pp. 1173-1185.
IEEE DOI
1903
Saliency detection, Semisupervised learning, Control systems,
Object detection, Visualization, Image color analysis, Cybernetics,
semi-supervised learning
BibRef
Zhou, Y.,
Mao, A.,
Huo, S.,
Lei, J.,
Kung, S.Y.,
Salient Object Detection via Fuzzy Theory and Object-Level
Enhancement,
MultMed(21), No. 1, January 2019, pp. 74-85.
IEEE DOI
1901
BibRef
Earlier: A3, A1, A5:
Salient object detection via a linear feedback control system,
ICIP17(4257-4261)
IEEE DOI
1803
Proposals, Saliency detection, Fuzzy sets, Object detection,
Fuzzy set theory, Optimization, Fuses, Saliency detection.
Image color analysis, Linear feedback control systems,
Mathematical model, Object detection.
BibRef
Zhou, Y.,
Zhang, T.,
Huo, S.,
Hou, C.,
Kung, S.,
Adaptive Irregular Graph Construction-Based Salient Object Detection,
CirSysVideo(30), No. 6, June 2020, pp. 1569-1582.
IEEE DOI
2006
Object detection, Visualization, Image color analysis,
Image segmentation, Computational modeling,
label propagation
BibRef
Tang, Y.X.[Yu-Xing],
Wang, J.[Josiah],
Wang, X.,
Gao, B.Y.[Bo-Yang],
Dellandréa, E.[Emmanuel],
Gaizauskas, R.[Robert],
Chen, L.M.[Li-Ming],
Visual and Semantic Knowledge Transfer for Large Scale
Semi-Supervised Object Detection,
PAMI(40), No. 12, December 2018, pp. 3045-3058.
IEEE DOI
1811
BibRef
Earlier: A1, A2, A4, A5, A6, A7, Only:
Large Scale Semi-Supervised Object Detection Using Visual and
Semantic Knowledge Transfer,
CVPR16(2119-2128)
IEEE DOI
1612
Semisupervised learning, Semantics,
Convolutional neural networks, Learning systems,
weakly supervised object detection
BibRef
Tang, P.[Peng],
Wang, X.G.[Xing-Gang],
Bai, S.[Song],
Shen, W.[Wei],
Bai, X.[Xiang],
Liu, W.Y.[Wen-Yu],
Yuille, A.L.[Alan L.],
PCL: Proposal Cluster Learning for Weakly Supervised Object Detection,
PAMI(42), No. 1, January 2020, pp. 176-191.
IEEE DOI
1912
Proposals, Training, Streaming media, Detectors, Object detection,
Convolutional neural networks, Object detection, proposal cluster
BibRef
Tang, P.[Peng],
Ramaiah, C.[Chetan],
Wang, Y.[Yan],
Xu, R.[Ran],
Xiong, C.M.[Cai-Ming],
Proposal Learning for Semi-Supervised Object Detection,
WACV21(2290-2300)
IEEE DOI
2106
Training, Detectors, Object detection,
Semisupervised learning, Feature extraction
BibRef
Tang, P.[Peng],
Wang, X.G.[Xing-Gang],
Wang, A.[Angtian],
Yan, Y.L.[Yong-Luan],
Liu, W.Y.[Wen-Yu],
Huang, J.Z.[Jun-Zhou],
Yuille, A.L.[Alan L.],
Weakly Supervised Region Proposal Network and Object Detection,
ECCV18(XI: 370-386).
Springer DOI
1810
BibRef
Jeong, J.[Jisoo],
Verma, V.[Vikas],
Hyun, M.[Minsung],
Kannala, J.H.[Ju-Ho],
Kwak, N.[Nojun],
Interpolation-based Semi-supervised Learning for Object Detection,
CVPR21(11597-11606)
IEEE DOI
2111
Interpolation, Supervised learning, Object detection, Detectors,
Computer architecture, Semisupervised learning, Benchmark testing
BibRef
Yang, Z.H.[Zhao-Hui],
Shi, M.J.[Miao-Jing],
Xu, C.[Chao],
Ferrari, V.[Vittorio],
Avrithis, Y.[Yannis],
Training object detectors from few weakly-labeled and many unlabeled
images,
PR(120), 2021, pp. 108164.
Elsevier DOI
2109
Object detection, Weakly-supervised learning,
Semi-supervised learning, Unlabelled set
BibRef
Chen, C.[Cong],
Dong, S.Y.[Shou-Yang],
Tian, Y.[Ye],
Cao, K.L.[Kun-Lin],
Liu, L.[Li],
Guo, Y.H.[Yuan-Hao],
Temporal Self-Ensembling Teacher for Semi-Supervised Object Detection,
MultMed(24), 2022, pp. 3679-3692.
IEEE DOI
2208
Object detection, Predictive models, Training, Detectors,
Data models, Analytical models, Transforms, focal loss
BibRef
Lv, Y.Q.[Yun-Qiu],
Liu, B.[Bowen],
Zhang, J.[Jing],
Dai, Y.C.[Yu-Chao],
Li, A.[Aixuan],
Zhang, T.[Tong],
Semi-supervised Active Salient Object Detection,
PR(123), 2022, pp. 108364.
Elsevier DOI
2112
Salient object detection, Annotation-efficient Learning,
Active learning, Variational Auto-Encoder
BibRef
Lv, P.[Pei],
Hu, S.[Suqi],
Hao, T.R.[Tian-Ran],
Contrastive Proposal Extension With LSTM Network for Weakly
Supervised Object Detection,
IP(31), 2022, pp. 6879-6892.
IEEE DOI
2212
Proposals, Semantics, Feature extraction, Object detection,
Recurrent neural networks, Training, Task analysis,
context awareness
BibRef
Ma, C.C.[Cheng-Cheng],
Pan, X.[Xingjia],
Ye, Q.X.[Qi-Xiang],
Tang, F.[Fan],
Dong, W.M.[Wei-Ming],
Xu, C.S.[Chang-Sheng],
CrossRectify: Leveraging disagreement for semi-supervised object
detection,
PR(137), 2023, pp. 109280.
Elsevier DOI
2302
Object detection, Semi-supervised learning,
2D Semi-supervised object detection, Self-labeling
BibRef
Li, G.[Gang],
Li, X.[Xiang],
Wang, Y.J.[Yu-Jie],
Wu, Y.C.[Yi-Chao],
Liang, D.[Ding],
Zhang, S.S.[Shan-Shan],
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised
Object Detection,
ECCV22(IX:457-472).
Springer DOI
2211
BibRef
Zhou, H.Y.[Hong-Yu],
Ge, Z.[Zheng],
Liu, S.T.[Song-Tao],
Mao, W.X.[Wei-Xin],
Li, Z.M.[Ze-Ming],
Yu, H.Y.[Hai-Yan],
Sun, J.[Jian],
Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection,
ECCV22(IX:35-50).
Springer DOI
2211
BibRef
Qi, L.[Lu],
Kuen, J.[Jason],
Lin, Z.[Zhe],
Gu, J.X.[Jiu-Xiang],
Rao, F.Y.[Feng-Yun],
Li, D.[Dian],
Guo, W.D.[Wei-Dong],
Wen, Z.[Zhen],
Yang, M.H.[Ming-Hsuan],
Jia, J.Y.[Jia-Ya],
CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and
Segmentation,
ECCV22(XXXI:59-77).
Springer DOI
2211
BibRef
Seo, J.W.[Jinh-Wan],
Bae, W.[Wonho],
Sutherland, D.J.[Danica J.],
Noh, J.[Junhyug],
Kim, D.J.[Dai-Jin],
Object Discovery via Contrastive Learning for Weakly Supervised Object
Detection,
ECCV22(XXXI:312-329).
Springer DOI
2211
BibRef
Huang, Z.T.[Zi-Tong],
Bao, Y.P.[Yi-Ping],
Dong, B.[Bowen],
Zhou, E.[Erjin],
Zuo, W.M.[Wang-Meng],
W2N: Switching from Weak Supervision to Noisy Supervision for Object
Detection,
ECCV22(XXX:708-724).
Springer DOI
2211
BibRef
Liu, Y.C.[Yen-Cheng],
Ma, C.Y.[Chih-Yao],
Dai, X.L.[Xiao-Liang],
Tian, J.J.[Jun-Jiao],
Vajda, P.[Peter],
He, Z.J.[Zi-Jian],
Kira, Z.[Zsolt],
Open-Set Semi-Supervised Object Detection,
ECCV22(XXX:143-159).
Springer DOI
2211
BibRef
Park, J.H.[Jin-Hyung],
Xu, C.F.[Chen-Feng],
Zhou, Y.Y.[Yi-Yang],
Tomizuka, M.[Masayoshi],
Zhan, W.[Wei],
DetMatch: Two Teachers are Better than One for Joint 2D and 3D
Semi-Supervised Object Detection,
ECCV22(X:370-389).
Springer DOI
2211
BibRef
Vo, H.V.[Huy V.],
Siméoni, O.[Oriane],
Gidaris, S.[Spyros],
Bursuc, A.[Andrei],
Pérez, P.[Patrick],
Ponce, J.[Jean],
Active Learning Strategies for Weakly-Supervised Object Detection,
ECCV22(XXX:211-230).
Springer DOI
2211
BibRef
Li, L.F.[Lin-Feng],
Jiang, M.Y.[Min-Yue],
Yu, Y.[Yue],
Zhang, W.[Wei],
Lin, X.R.[Xiang-Ru],
Li, Y.Y.[Ying-Ying],
Tan, X.[Xiao],
Wang, J.D.[Jing-Dong],
Ding, E.[Errui],
Diverse Learner: Exploring Diverse Supervision for Semi-supervised
Object Detection,
ECCV22(XXX:640-655).
Springer DOI
2211
BibRef
Chen, C.[Changrui],
Debattista, K.[Kurt],
Han, J.G.[Jun-Gong],
Semi-supervised Object Detection via VC Learning,
ECCV22(XXXI:169-185).
Springer DOI
2211
BibRef
Tanaka, Y.[Yuki],
Yoshida, S.M.[Shuhei M.],
Terao, M.[Makoto],
Non-Iterative Optimization of Pseudo-Labeling Thresholds for Training
Object Detection Models from Multiple Datasets,
ICIP22(1676-1680)
IEEE DOI
2211
Training, Deep learning, Costs, Computational modeling,
Supervised learning, Object detection, weakly supervised learning
BibRef
Chang, Q.[Qing],
Peng, J.[Junran],
Xie, L.X.[Ling-Xi],
Sun, J.J.[Jia-Jun],
Yin, H.R.[Hao-Ran],
Tian, Q.[Qi],
Zhang, Z.X.[Zhao-Xiang],
DATA: Domain-Aware and Task-Aware Self-supervised Learning,
CVPR22(9831-9840)
IEEE DOI
2210
Training, Measurement, Costs, Computational modeling,
Self-supervised learning, Object detection, Market research,
Self- semi- meta- Deep learning architectures and techniques
BibRef
Liu, Y.Y.[Yu-Yuan],
Tian, Y.[Yu],
Chen, Y.H.[Yuan-Hong],
Liu, F.[Fengbei],
Belagiannis, V.[Vasileios],
Carneiro, G.[Gustavo],
Perturbed and Strict Mean Teachers for Semi-supervised Semantic
Segmentation,
CVPR22(4248-4257)
IEEE DOI
2210
Training, Image segmentation, Shape, Perturbation methods, Semantics,
Mean square error methods, Predictive models, Segmentation,
Self- semi- meta- unsupervised learning
BibRef
Yu, J.[Jun],
Zhang, L.W.[Li-Wen],
Du, S.S.[Shen-Shen],
Chang, H.[Hao],
Lu, K.[Keda],
Zhang, Z.[Zhong],
Yu, Y.[Ye],
Wang, L.[Lei],
Ling, Q.[Qiang],
Pseudo-label Generation and Various Data Augmentation for
Semi-Supervised Hyperspectral Object Detection,
PBVS22(304-311)
IEEE DOI
2210
Training, Object detection, Semisupervised learning,
Data models, Pattern recognition
BibRef
Kim, J.M.[Jong-Mok],
Jang, J.Y.[Joo-Young],
Seo, S.[Seunghyeon],
Jeong, J.[Jisoo],
Na, J.[Jongkeun],
Kwak, N.[Nojun],
MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised
Object Detection,
CVPR22(14492-14501)
IEEE DOI
2210
Protocols, Semantics, Object detection, Benchmark testing,
Semisupervised learning, Feature extraction, Transformers, retrieval
BibRef
Wang, P.[Pei],
Cai, Z.W.[Zhao-Wei],
Yang, H.[Hao],
Swaminathan, G.[Gurumurthy],
Vasconcelos, N.M.[Nuno M.],
Schiele, B.[Bernt],
Soatto, S.[Stefano],
Omni-DETR: Omni-Supervised Object Detection with Transformers,
CVPR22(9357-9366)
IEEE DOI
2210
Costs, Codes, Annotations, Filtering, Object detection,
Computer architecture, Recognition: detection, categorization, retrieval
BibRef
Zhang, S.L.[Shi-Long],
Yu, Z.R.[Zhuo-Ran],
Liu, L.Y.[Li-Yang],
Wang, X.J.[Xin-Jiang],
Zhou, A.[Aojun],
Chen, K.[Kai],
Group R-CNN for Weakly Semi-supervised Object Detection with Points,
CVPR22(9407-9416)
IEEE DOI
2210
Representation learning, Image recognition, Annotations,
Training data, Object detection, Detectors, Transformers,
retrieval
BibRef
Li, A.[Aoxue],
Yuan, P.[Peng],
Li, Z.G.[Zhen-Guo],
Semi-Supervised Object Detection via Multi-instance Alignment with
Global Class Prototypes,
CVPR22(9799-9808)
IEEE DOI
2210
Training, Computational modeling, Prototypes, Detectors,
Object detection, Predictive models,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Liu, Y.C.[Yen-Cheng],
Ma, C.Y.[Chih-Yao],
Kira, Z.[Zsolt],
Unbiased Teacher v2: Semi-supervised Object Detection for Anchor-free
and Anchor-based Detectors,
CVPR22(9809-9818)
IEEE DOI
2210
Training, Uncertainty, Detectors, Object detection,
Benchmark testing, Pattern recognition,
Vision applications and systems
BibRef
Chen, B.B.[Bin-Bin],
Chen, W.J.[Wei-Jie],
Yang, S.[Shicai],
Xuan, Y.[Yunyi],
Song, J.[Jie],
Xie, D.[Di],
Pu, S.L.[Shi-Liang],
Song, M.L.[Ming-Li],
Zhuang, Y.T.[Yue-Ting],
Label Matching Semi-Supervised Object Detection,
CVPR22(14361-14370)
IEEE DOI
2210
Adaptation models, Monte Carlo methods, Codes,
Computational modeling, Object detection, Proposals,
Self- semi- meta- unsupervised learning
BibRef
Mi, P.[Peng],
Lin, J.[Jianghang],
Zhou, Y.[Yiyi],
Shen, Y.[Yunhang],
Luo, G.[Gen],
Sun, X.S.[Xiao-Shuai],
Cao, L.J.[Liu-Juan],
Fu, R.R.[Rong-Rong],
Xu, Q.[Qiang],
Ji, R.R.[Rong-Rong],
Active Teacher for Semi-Supervised Object Detection,
CVPR22(14462-14471)
IEEE DOI
2210
Training, Annotations, Computational modeling, Surveillance,
Object detection, Performance gain, Inspection,
Self- semi- meta- unsupervised learning
BibRef
Guo, Q.S.[Qiu-Shan],
Mu, Y.[Yao],
Chen, J.Y.[Jian-Yu],
Wang, T.Q.[Tian-Qi],
Yu, Y.Z.[Yi-Zhou],
Luo, P.[Ping],
Scale-Equivalent Distillation for Semi-Supervised Object Detection,
CVPR22(14502-14511)
IEEE DOI
2210
Location awareness, Estimation, Object detection, Detectors,
Semisupervised learning, Data models, Pattern recognition, retrieval
BibRef
Rossi, L.[Leonardo],
Karimi, A.[Akbar],
Prati, A.[Andrea],
Improving Localization for Semi-Supervised Object Detection,
CIAP22(II:516-527).
Springer DOI
2205
BibRef
Chen, L.Y.[Liang-Yu],
Yang, T.[Tong],
Zhang, X.Y.[Xiang-Yu],
Zhang, W.[Wei],
Sun, J.[Jian],
Points as Queries: Weakly Semi-supervised Object Detection by Points,
CVPR21(8819-8828)
IEEE DOI
2111
Measurement, Annotations, Detectors,
Object detection, Pattern recognition, Task analysis
BibRef
Gong, C.Y.[Cheng-Yue],
Wang, D.[Dilin],
Liu, Q.[Qiang],
AlphaMatch: Improving Consistency for Semi-supervised Learning with
Alpha-divergence,
CVPR21(13678-13687)
IEEE DOI
2111
Object detection, Machine learning, Semisupervised learning,
Benchmark testing, Iterative algorithms, Pattern recognition,
Machine translation
BibRef
Zhao, N.,
Chua, T.,
Lee, G.H.,
SESS: Self-Ensembling Semi-Supervised 3D Object Detection,
CVPR20(11076-11084)
IEEE DOI
2008
Object detection,
Perturbation methods, Proposals, Task analysis, Training
BibRef
Rosenberg, C.[Chuck],
Hebert, M.[Martial],
Schneiderman, H.[Henry],
Semi-Supervised Self-Training of Object Detection Models,
WACV05(I: 29-36).
IEEE DOI
0502
BibRef
Rosenberg, C.,
Hebert, M.,
Training Object Detection Models with Weakly Labeled Data,
BMVC02(Poster Session).
0208
BibRef
Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Instance of Particular Object, Specified Object .