7.1.7.8 Semi-Supervised Object Detection

Chapter Contents (Back)
Object Detction. Semi-Supervised Object Detection.
See also Semi-Supervised Object Detection, 3D Object Detection.
See also Weakly Supervised, Unsupervised Salient Regions.
See also Dense Object Detection.
See also Semi-Supervised Clustering, Semi-Supervised Learning, Classification.
See also Self-Supervised Learning for Object Detection and Segmentation.

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.[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, 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


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

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

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

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, 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

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.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, 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

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.[Chaohui], 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 .


Last update:Jul 18, 2024 at 20:50:34