Zhou, Y.[Yuan],
Huo, S.W.[Shu-Wei],
Xiang, W.[Wei],
Hou, C.P.[Chun-Ping],
Kung, S.Y.[Sun-Yuan],
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.J.[Xing-Jia],
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, S.J.[Shi-Jie],
Liu, J.M.[Jun-Min],
Shen, W.L.[Wei-Lin],
Sun, J.Y.[Jian-Yong],
Tan, C.L.[Chang-Le],
Robust Teacher: Self-correcting pseudo-label-guided semi-supervised
learning for object detection,
CVIU(235), 2023, pp. 103788.
Elsevier DOI
2310
Object detection, Semi-supervised learning, Pseudo-labels,
Deep learning, Computer vision
BibRef
Hazra, S.[Somnath],
Dasgupta, P.[Pallab],
Penalizing proposals using classifiers for semi-supervised object
detection,
CVIU(235), 2023, pp. 103772.
Elsevier DOI
2310
Learning for vision, Semi-supervised learning, Object detection
BibRef
Chun, D.[Dayoung],
Lee, S.[Seungil],
Kim, H.[Hyun],
USD: Uncertainty-Based One-Phase Learning to Enhance Pseudo-Label
Reliability for Semi-Supervised Object Detection,
MultMed(26), 2024, pp. 6336-6347.
IEEE DOI
2404
Training, Annotations, Uncertainty, Object detection, Reliability,
Task analysis, Predictive models, Deep learning, object detection,
uncertainty
BibRef
Shanmugasundaram, S.[Suresh],
Palaniappan, N.[Natarajan],
Detection Accuracy Improvement on One-Stage Object Detection Using
AP-Loss-Based Ranking Module and ResNet-152 Backbone,
IJIG(24), No. 3, May 2024, pp. 2450030.
DOI Link
2406
BibRef
Wang, H.[Hao],
Jia, T.[Tong],
Wang, Q.L.[Qi-Long],
Zuo, W.M.[Wang-Meng],
Relation Knowledge Distillation by Auxiliary Learning for Object
Detection,
IP(33), 2024, pp. 4796-4810.
IEEE DOI
2409
Task analysis, Predictive models, Object detection,
Location awareness, Adaptation models, Accuracy, Head,
auxiliary learning
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
Yang, X.[Xi],
Li, P.H.[Peng-Hui],
Zhou, Q.[Qiubai],
Wang, N.N.[Nan-Nan],
Gao, X.B.[Xin-Bo],
Dense Information Learning Based Semi-Supervised Object Detection,
IP(34), 2025, pp. 1022-1035.
IEEE DOI
2502
Object detection, Training, Semisupervised learning,
Perturbation methods, Detectors, Data models, Accuracy,
object detection
BibRef
Liu, B.[Bo],
Yang, C.R.[Cheng-Rong],
Guo, J.[Jing],
Yang, Y.[Yun],
A Novel Semi-Supervised Object Detection Approach via Scale
Rebalancing and Global Proposal Contrast Consistency,
CirSysVideo(35), No. 1, January 2025, pp. 232-244.
IEEE DOI
2502
Proposals, Object detection, Training, Optimization, Accuracy,
Location awareness, Contrastive learning,
global proposal consistency comparison
BibRef
Marvasti-Zadeh, S.M.[Seyed Mojtaba],
Ray, N.[Nilanjan],
Erbilgin, N.[Nadir],
Training-Based Model Refinement and Representation Disagreement for
Semi-Supervised Object Detection,
WACV24(2245-2254)
IEEE DOI
2404
Training, Object detection, Detectors, Data models, Reliability,
Noise measurement, Algorithms, Machine learning architectures,
Image recognition and understanding
BibRef
Hua, W.[Wei],
Liang, D.K.[Ding-Kang],
Li, J.Y.[Jing-Yu],
Liu, X.L.[Xiao-Long],
Zou, Z.K.[Zhi-Kang],
Ye, X.Q.[Xiao-Qing],
Bai, X.[Xiang],
SOOD: Towards Semi-Supervised Oriented Object Detection,
CVPR23(15558-15567)
IEEE DOI
2309
BibRef
Wang, X.J.[Xin-Jiang],
Yang, X.Y.[Xing-Yi],
Zhang, S.L.[Shi-Long],
Li, Y.J.[Yi-Jiang],
Feng, L.T.[Li-Tong],
Fang, S.J.[Shi-Jie],
Lyu, C.Q.[Cheng-Qi],
Chen, K.[Kai],
Zhang, W.[Wayne],
Consistent-Teacher: Towards Reducing Inconsistent Pseudo-Targets in
Semi-Supervised Object Detection,
CVPR23(3240-3249)
IEEE DOI
2309
BibRef
Liu, L.A.[Li-Ang],
Zhang, B.[Boshen],
Zhang, J.N.[Jiang-Ning],
Zhang, W.[Wuhao],
Gan, Z.Y.[Zhen-Ye],
Tian, G.Z.[Guan-Zhong],
Zhu, W.B.[Wen-Bing],
Wang, Y.[Yabiao],
Wang, C.J.[Cheng-Jie],
MixTeacher: Mining Promising Labels with Mixed Scale Teacher for
Semi-Supervised Object Detection,
CVPR23(7370-7379)
IEEE DOI
2309
BibRef
Gungor, C.[Cagri],
Kovashka, A.[Adriana],
Complementary Cues from Audio Help Combat Noise in Weakly-Supervised
Object Detection,
WACV23(2184-2193)
IEEE DOI
2302
Training, Location awareness, Visualization, Music, Object detection,
Detectors, Vision + language and/or other modalities
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
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.R.[Chang-Rui],
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,
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
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
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,
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.H.[Jiang-Hang],
Zhou, Y.[Yiyi],
Shen, Y.H.[Yun-Hang],
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, 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, 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,
Machine translation
BibRef
Tang, Y.H.[Yi-He],
Chen, W.F.[Wei-Feng],
Luo, Y.J.[Yi-Jun],
Zhang, Y.T.[Yu-Ting],
Humble Teachers Teach Better Students for Semi-Supervised Object
Detection,
CVPR21(3131-3140)
IEEE DOI
2111
Training, Object detection, Detectors,
Benchmark testing, Feature extraction, Data models
BibRef
Yoon, J.[Jihun],
Hong, S.[Seungbum],
Choi, M.K.[Min-Kook],
Semi-Supervised Object Detection With Sparsely Annotated Dataset,
ICIP21(719-723)
IEEE DOI
2201
Training, Degradation, Annotations, Statistical analysis,
Image processing, Object detection, Detectors, Object detection,
sparse annotation
BibRef
Yang, Q.Z.[Qi-Ze],
Wei, X.H.[Xi-Han],
Wang, B.[Biao],
Hua, X.S.[Xian-Sheng],
Zhang, L.[Lei],
Interactive Self-Training with Mean Teachers for Semi-supervised
Object Detection,
CVPR21(5937-5946)
IEEE DOI
2111
Training, Head, Costs, Fuses, Object detection, Predictive models
BibRef
Wang, Z.Y.[Zhen-Yu],
Li, Y.[Yali],
Guo, Y.[Ye],
Fang, L.[Lu],
Wang, S.J.[Sheng-Jin],
Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised
Object Detection,
CVPR21(4566-4575)
IEEE DOI
2111
Training, Learning systems, Uncertainty,
Upper bound, Object detection, Detectors
BibRef
Zhou, Q.[Qiang],
Yu, C.H.[Chao-Hui],
Wang, Z.B.[Zhi-Bin],
Qian, Q.[Qi],
Li, H.[Hao],
Instant-Teaching:
An End-to-End Semi-Supervised Object Detection Framework,
CVPR21(4079-4088)
IEEE DOI
2111
Training, Annotations, Supervised learning,
Object detection, Detectors, Manuals
BibRef
Fu, Y.[Yun],
Li, Z.[Zhu],
Zhou, X.[Xi],
Huang, T.S.[Thomas S.],
Laplacian Affinity Propagation for Semi-Supervised Object
Classification,
ICIP07(I: 189-192).
IEEE DOI
0709
graph-based learning algorithm
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 .