14.1.12 Multi-Label Classification, Multilabel Classification

Zhang, M.L.[Min-Ling], Zhou, Z.H.[Zhi-Hua],
ML-KNN: A lazy learning approach to multi-label learning,
PR(40), No. 7, July 2007, pp. 2038-2048.
Elsevier DOI 0704
Machine learning; Multi-label learning; Lazy learning; K-nearest neighbor; Functional genomics; Natural scene classification; Text categorization BibRef

Park, C.H.[Cheong Hee], Lee, M.H.[Moon-Hwi],
On applying linear discriminant analysis for multi-labeled problems,
PRL(29), No. 7, 1 May 2008, pp. 878-887.
Elsevier DOI 0804
Dimension reduction; Linear discriminant analysis; Multi-labeled problems; Text categorization BibRef

Han, Y., Wu, F., Zhuang, Y.T., He, X.,
Multi-Label Transfer Learning With Sparse Representation,
CirSysVideo(20), No. 8, August 2010, pp. 1110-1121.
IEEE DOI 1008
BibRef

Diez, J., del Coz, J.J., Bahamonde, A.,
A semi-dependent decomposition approach to learn hierarchical classifiers,
PR(43), No. 11, November 2010, pp. 3795-3804.
Elsevier DOI 1008
Hierarchical classification; Multi-label learning; Structured output classification; Cost-sensitive learning; Support vector machines BibRef

Barranquero, J.[Jose], Díez, J.[Jorge], del Coz, J.J.[Juan José],
Quantification-oriented learning based on reliable classifiers,
PR(48), No. 2, 2015, pp. 591-604.
Elsevier DOI 1411
Quantification BibRef

Xu, J.H.[Jian-Hua],
Fast multi-label core vector machine,
PR(46), No. 3, March 2013, pp. 885-898.
Elsevier DOI 1212
Support vector machine; Core vector machine; Multi-label classification; Frank-Wolfe method; Linear programming; Quadratic programming BibRef

Xu, J.H.[Jian-Hua],
Multi-label core vector machine with a zero label,
PR(47), No. 7, 2014, pp. 2542-2557.
Elsevier DOI 1404
Multi-label classification BibRef

Park, S.[Sunho], Choi, S.J.[Seung-Jin],
Max-margin embedding for multi-label learning,
PRL(34), No. 3, 1 February 2013, pp. 292-298.
Elsevier DOI 1301
Multi-label learning; Label embedding; Max-margin learning; Cost-sensitive multi-label hinge loss; One versus all (OVA) BibRef

Yuan, Y.[Ying], Wu, F.[Fei], Shao, J.[Jian], Zhuang, Y.T.[Yue-Ting],
Image annotation by semi-supervised cross-domain learning with group sparsity,
JVCIR(24), No. 2, February 2013, pp. 95-102.
Elsevier DOI 1302
Cross-domain; Manifold regularization; Group sparsity; Multiple kernel learning; Multi-label; Image annotation; Semi-supervise; Discriminant analysis BibRef

Kocev, D.[Dragi], Vens, C.[Celine], Struyf, J.[Jan], Džeroski, S.[Sašo],
Tree ensembles for predicting structured outputs,
PR(46), No. 3, March 2013, pp. 817-833.
Elsevier DOI 1212
Ensemble methods; Predictive clustering trees; Structured outputs; Multi-target regression; Multi-target classification; Hierarchical multi-label classification BibRef

Naula, P.[Pekka], Airola, A.[Antti], Salakoski, T.[Tapio], Pahikkala, T.[Tapio],
Multi-label learning under feature extraction budgets,
PRL(40), No. 1, 2014, pp. 56-65.
Elsevier DOI 1403
Feature selection BibRef

Wang, S.F.[Shang-Fei], Wang, J.[Jun], Wang, Z.Y.[Zhao-Yu], Ji, Q.A.[Qi-Ang],
Enhancing multi-label classification by modeling dependencies among labels,
PR(47), No. 10, 2014, pp. 3405-3413.
Elsevier DOI 1406
Multi-label classification BibRef

Montañes, E.[Elena], Senge, R.[Robin], Barranquero, J.[Jose], Quevedo, J.R.[José Ramón], del Coz, J.J.[Juan José], Hüllermeier, E.[Eyke],
Dependent binary relevance models for multi-label classification,
PR(47), No. 3, 2014, pp. 1494-1508.
Elsevier DOI 1312
Multi-label classification BibRef

Wever, M.[Marcel], Tornede, A.[Alexander], Mohr, F.[Felix], Hüllermeier, E.[Eyke],
AutoML for Multi-Label Classification: Overview and Empirical Evaluation,
PAMI(43), No. 9, September 2021, pp. 3037-3054.
IEEE DOI 2108
Tools, Pipelines, Machine learning, Loss measurement, Search problems, Complexity theory, Training, Bayesian optimization BibRef

Madjarov, G.[Gjorgji], Gjorgjevikj, D.[Dejan], Džeroski, S.[Sašo],
Two stage architecture for multi-label learning,
PR(45), No. 3, March 2012, pp. 1019-1034.
Elsevier DOI 1111
BibRef
And:
Dual Layer Voting Method for Efficient Multi-label Classification,
IbPRIA11(232-239).
Springer DOI 1106
Multi-label learning; Multi-label ranking; Multi-label classification; Two stage architecture; Classifier chain BibRef

Madjarov, G.[Gjorgji], Kocev, D.[Dragi], Gjorgjevikj, D.[Dejan], Džeroski, S.[Sašo],
An extensive experimental comparison of methods for multi-label learning,
PR(45), No. 9, September 2012, pp. 3084-3104.
Elsevier DOI 1206
Multi-label ranking; Multi-label classification; Comparison of multi-label learning methods BibRef

Luo, Y., Tao, D.C., Geng, B., Xu, C., Maybank, S.J.,
Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification,
IP(22), No. 2, February 2013, pp. 523-536.
IEEE DOI 1302
BibRef

Gong, C., Tao, D., Maybank, S.J., Liu, W., Kang, G., Yang, J.,
Multi-Modal Curriculum Learning for Semi-Supervised Image Classification,
IP(25), No. 7, July 2016, pp. 3249-3260.
IEEE DOI 1606
image classification BibRef

Liu, Q., Sun, Y., Wang, C., Liu, T., Tao, D.,
Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification,
IP(26), No. 1, January 2017, pp. 452-463.
IEEE DOI 1612
graph theory BibRef

Quevedo, J.R.[José Ramón], Luaces, O.[Oscar], Bahamonde, A.[Antonio],
Multilabel classifiers with a probabilistic thresholding strategy,
PR(45), No. 2, February 2012, pp. 876-883.
Elsevier DOI 1110
Multilabel classification; Thresholding strategies; Posterior probability; Expected loss BibRef

Lastra, G.[Gerardo], Luaces, O.[Oscar], Bahamonde, A.[Antonio],
Interval prediction for graded multi-label classification,
PRL(49), No. 1, 2014, pp. 171-176.
Elsevier DOI 1410
Graded multi-label classification BibRef

Dimitrovski, I.[Ivica], Kocev, D.[Dragi], Loskovska, S.[Suzana], Džeroski, S.[Sašo],
Fast and efficient visual codebook construction for multi-label annotation using predictive clustering trees,
PRL(38), No. 1, 2014, pp. 38-45.
Elsevier DOI 1402
Automatic image annotation BibRef

Sun, F.M.[Fu-Ming], Tang, J.H.[Jin-Hui], Li, H.J.[Hao-Jie], Qi, G.J.[Guo-Jun], Huang, T.S.,
Multi-Label Image Categorization With Sparse Factor Representation,
IP(23), No. 3, March 2014, pp. 1028-1037.
IEEE DOI 1403
multi-label classification reveals underlying correlations. correlation methods BibRef

Pei, Y.L.[Yuan-Li], Fern, X.L.Z.[Xiao-Li Z.],
Constrained instance clustering in multi-instance multi-label learning,
PRL(37), No. 1, 2014, pp. 107-114.
Elsevier DOI 1402
MIML BibRef

Pham, A.T., Raich, R., Fern, X.Z.,
Dynamic Programming for Instance Annotation in Multi-Instance Multi-Label Learning,
PAMI(39), No. 12, December 2017, pp. 2381-2394.
IEEE DOI 1711
Computational modeling, Data models, Dynamic programming, Graphical models, Labeling, Probabilistic logic, Multi-instance multi-label learning, expectation maximization, instance annotation BibRef

Sucar, L.E.[L. Enrique], Bielza, C.[Concha], Morales, E.F.[Eduardo F.], Hernandez-Leal, P.[Pablo], Zaragoza, J.H.[Julio H.], Larrañaga, P.[Pedro],
Multi-label classification with Bayesian network-based chain classifiers,
PRL(41), No. 1, 2014, pp. 14-22.
Elsevier DOI 1403
Multi-label classification BibRef

Zhang, Q., Chen, L., Li, B.,
Max-Margin Multiattribute Learning With Low-Rank Constraint,
IP(23), No. 7, July 2014, pp. 2866-2876.
IEEE DOI 1407
Accuracy BibRef

Gönen, M.[Mehmet],
Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning,
PRL(38), No. 1, 2014, pp. 132-141.
Elsevier DOI 1402
Multilabel learning BibRef

Zhang, M.L.[Min-Ling], Wu, L.[Lei],
Lift: Multi-Label Learning with Label-Specific Features,
PAMI(37), No. 1, January 2015, pp. 107-120.
IEEE DOI 1412
Algorithm design and analysis BibRef

Hang, J.Y.[Jun-Yi], Zhang, M.L.[Min-Ling],
Collaborative Learning of Label Semantics and Deep Label-Specific Features for Multi-Label Classification,
PAMI(44), No. 12, December 2022, pp. 9860-9871.
IEEE DOI 2212
Semantics, Feature extraction, Correlation, Deep learning, Representation learning, Encoding, Collaboration, Machine learning, collaborative learning BibRef

Yu, Z.B.[Ze-Bang], Zhang, M.L.[Min-Ling],
Multi-Label Classification With Label-Specific Feature Generation: A Wrapped Approach,
PAMI(44), No. 9, September 2022, pp. 5199-5210.
IEEE DOI 2208
Correlation, Task analysis, Wrapping, Training, Predictive models, Optimization, Analytical models, Multi-label classification, wrapped procedure BibRef

Kim, M.Y.[Min-Young],
Multiple-concept feature generative models for multi-label image classification,
CVIU(136), No. 1, 2015, pp. 69-78.
Elsevier DOI 1506
Probabilistic graphical models BibRef

Kim, M.Y.[Min-Young],
Sparse conditional copula models for structured output regression,
PR(60), No. 1, 2016, pp. 761-769.
Elsevier DOI 1609
Multiple output regression BibRef

Lim, H.K.[Hyun-Ki], Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Multi-Label Learning Using Mathematical Programming,
IEICE(E98-D), No. 1, January 2015, pp. 197-200.
WWW Link. 1503
BibRef

Li, X.M.[Xi-Ming], Ouyang, J.H.[Ji-Hong], Zhou, X.T.[Xiao-Tang],
Centroid prior topic model for multi-label classification,
PRL(62), No. 1, 2015, pp. 8-13.
Elsevier DOI 1507
Multi-label classification BibRef

Destercke, S.[Sébastien],
Multilabel predictions with sets of probabilities: The Hamming and ranking loss cases,
PR(48), No. 11, 2015, pp. 3757-3765.
Elsevier DOI 1506
Multilabel BibRef

Ivasic-Kos, M.[Marina], Pobar, M.[Miran], Ribaric, S.[Slobodan],
Two-tier image annotation model based on a multi-label classifier and fuzzy-knowledge representation scheme,
PR(52), No. 1, 2016, pp. 287-305.
Elsevier DOI 1601
Image annotation BibRef

Li, X., Zhao, X., Zhang, Z., Wu, F., Zhuang, Y., Wang, J., Li, X.,
Joint Multilabel Classification With Community-Aware Label Graph Learning,
IP(25), No. 1, January 2016, pp. 484-493.
IEEE DOI 1601
Computer vision BibRef

Triguero, I.[Isaac], Vens, C.[Celine],
Labelling strategies for hierarchical multi-label classification techniques,
PR(56), No. 1, 2016, pp. 170-183.
Elsevier DOI 1604
Hierarchical multi-label classification BibRef

Jing, X.Y., Wu, F., Li, Z., Hu, R., Zhang, D.,
Multi-Label Dictionary Learning for Image Annotation,
IP(25), No. 6, June 2016, pp. 2712-2725.
IEEE DOI 1605
Computational modeling BibRef

Li, Q.[Qiang], Xie, B.[Bo], You, J.[Jane], Bian, W.[Wei], Tao, D.C.[Da-Cheng],
Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification,
IP(25), No. 8, August 2016, pp. 3801-3813.
IEEE DOI 1608
correlation methods BibRef

Pu, J.M.[Jia-Meng], Zhang, Q., Zhang, L., Du, B., You, J.[Jane],
Multiview clustering based on Robust and Regularized Matrix Approximation,
ICPR16(2550-2555)
IEEE DOI 1705
Approximation algorithms, Clustering algorithms, Linear programming, Manifolds, Matrix decomposition, Optimization, Robustness BibRef

Liu, B.[Bin], Xu, Z.L.[Zeng-Lin], Wu, S.[Shuang], Wang, F.[Fei],
Manifold regularized matrix completion for multilabel classification,
PRL(80), No. 1, 2016, pp. 58-63.
Elsevier DOI 1609
Multi-label learning BibRef

Pillai, I.[Ignazio], Fumera, G.[Giorgio], Roli, F.[Fabio],
Designing multi-label classifiers that maximize F measures: State of the art,
PR(61), No. 1, 2017, pp. 394-404.
Elsevier DOI 1705
BibRef
Earlier:
Learning of Multilabel Classifiers,
ICPR14(3452-3456)
IEEE DOI 1412
Multi-label classification. Algorithm design and analysis BibRef

Read, J.[Jesse], Martino, L.[Luca], Hollmén, J.[Jaakko],
Multi-label methods for prediction with sequential data,
PR(63), No. 1, 2017, pp. 45-55.
Elsevier DOI 1612
Multi-label classification BibRef

Wang, M., Luo, C., Hong, R., Tang, J., Feng, J.,
Beyond Object Proposals: Random Crop Pooling for Multi-Label Image Recognition,
IP(25), No. 12, December 2016, pp. 5678-5688.
IEEE DOI 1612
image recognition BibRef

Xia, Y., Nie, L., Zhang, L., Yang, Y., Hong, R., Li, X.,
Weakly Supervised Multilabel Clustering and its Applications in Computer Vision,
Cyber(46), No. 12, December 2016, pp. 3220-3232.
IEEE DOI 1612
Clustering algorithms BibRef

Du, B.[Bo], Wang, Z., Zhang, L.F.[Le-Fei], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng],
Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion,
IP(26), No. 4, April 2017, pp. 1694-1707.
IEEE DOI 1704
data mining BibRef

Wang, Z.M.[Zeng-Mao], Du, B.[Bo], Zhang, L.F.[Le-Fei], Zhang, L.P.[Liang-Pei], Fang, M.[Meng], Tao, D.C.[Da-Cheng],
Multi-label Active Learning Based on Maximum Correntropy Criterion: Towards Robust and Discriminative Labeling,
ECCV16(III: 453-468).
Springer DOI 1611
BibRef

Akbarnejad, A.[Amirhossein], Baghshah, M.S.[Mahdieh Soleymani],
A probabilistic multi-label classifier with missing and noisy labels handling capability,
PRL(89), No. 1, 2017, pp. 18-24.
Elsevier DOI 1704
Multi-label classification BibRef

Wu, J.[Jian], Ye, C.[Chen], Sheng, V.S.[Victor S.], Zhang, J.[Jing], Zhao, P.P.[Peng-Peng], Cui, Z.M.[Zhi-Ming],
Active learning with label correlation exploration for multi-label image classification,
IET-CV(11), No. 7, October 2017, pp. 577-584.
DOI Link 1709
BibRef
Earlier: A1, A3, A4, A5, A6, Only:
Multi-label active learning for image classification,
ICIP14(5227-5231)
IEEE DOI 1502
BibRef
And: A2, A1, A3, A5, A6, Only:
Multi-label active learning with label correlation for image classification,
ICIP15(3437-3441)
IEEE DOI 1512
Accuracy. Multi-label BibRef

Barezi, E.J.[Elham J.], Kwok, J.T.[James T.], Rabiee, H.R.[Hamid R.],
Multi-Label learning in the independent label sub-spaces,
PRL(97), No. 1, 2017, pp. 8-12.
Elsevier DOI 1709
BibRef
And: Corrigendum: PRL(112), 2018, pp. 152.
Elsevier DOI 1809
Large scale learning BibRef

Sun, L.[Lu], Kudo, M.[Mineichi],
Optimization of classifier chains via conditional likelihood maximization,
PR(74), No. 1, 2018, pp. 503-517.
Elsevier DOI 1711
Multi-label classification BibRef

Trajdos, P.[Pawel], Kurzynski, M.[Marek],
Weighting scheme for a pairwise multi-label classifier based on the fuzzy confusion matrix,
PRL(103), 2018, pp. 60-67.
Elsevier DOI 1802
Multi-label classification, Label pairwise transformation, Random reference classifier, Confusion matrix, Entropy BibRef

Zhang, H.[Hu], Wu, W.[Wei], Wang, D.[Ding],
Multi-instance multi-label learning of natural scene images: via sparse coding and multi-layer neural network,
IET-CV(12), No. 3, April 2018, pp. 305-311.
DOI Link 1804
BibRef

Wang, S.F.[Shang-Fei], Chen, S.Y.[Shi-Yu], Chen, T.F.[Tan-Fang], Shi, X.X.[Xiao-Xiao],
Learning with privileged information for multi-Label classification,
PR(81), 2018, pp. 60-70.
Elsevier DOI 1806
Privileged information, Multi-label classification, Similarity constraints BibRef

Ahmadi, Z.[Zahra], Kramer, S.[Stefan],
A label compression method for online multi-label classification,
PRL(111), 2018, pp. 64-71.
Elsevier DOI 1808
Data stream classification, Multi-label data, Label compression BibRef

Zhang, J., Wu, Q., Shen, C., Zhang, J., Lu, J.,
Multilabel Image Classification With Regional Latent Semantic Dependencies,
MultMed(20), No. 10, October 2018, pp. 2801-2813.
IEEE DOI 1810
image classification, learning (artificial intelligence), recurrent neural nets, multilabel image classification, deep neural network BibRef

Jin, C.[Cong], Jin, S.W.[Shu-Wei],
Multi-label automatic image annotation approach based on multiple improvement strategies,
IET-IPR(13), No. 4, March 2019, pp. 623-633.
DOI Link 1903
BibRef

Kumar, V.[Vikas], Pujari, A.K.[Arun K], Padmanabhan, V.[Vineet], Kagita, V.R.[Venkateswara Rao],
Group preserving label embedding for multi-label classification,
PR(90), 2019, pp. 23-34.
Elsevier DOI 1903
Multi-label classification, Label embedding, Matrix factorization BibRef

Nguyen, T.T.[Tien Thanh], Nguyen, T.T.T.[Thi Thu Thuy], Luong, A.V.[Anh Vu], Nguyen, Q.V.H.[Quoc Viet Hung], Liew, A.W.C.[Alan Wee-Chung], Stantic, B.[Bela],
Multi-label classification via label correlation and first order feature dependance in a data stream,
PR(90), 2019, pp. 35-51.
Elsevier DOI 1903
Multi-label classification, Multi-label learning, Online learning, Data stream, Concept drift, Label correlation, Feature dependence BibRef

Yu, W.J.[Wan-Jin], Chen, Z.D.[Zhen-Duo], Luo, X.[Xin], Liu, W.[Wu], Xu, X.S.[Xin-Shun],
DELTA: A deep dual-stream network for multi-label image classification,
PR(91), 2019, pp. 322-331.
Elsevier DOI 1904
Deep neural network, Dual-stream network, Multi-label image classification, Multi-instance learning BibRef

Zhang, J.[Juli], Zhang, J.[Junyi], Dai, T.[Tao], He, Z.Z.[Zhan-Zhuang],
Exploring Weighted Dual Graph Regularized Non-Negative Matrix Tri-Factorization Based Collaborative Filtering Framework for Multi-Label Annotation of Remote Sensing Images,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
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Lyu, F.[Fan], Wu, Q.[Qi], Hu, F.Y.[Fu-Yuan], Wu, Q.Y.[Qing-Yao], Tan, M.K.[Ming-Kui],
Attend and Imagine: Multi-Label Image Classification With Visual Attention and Recurrent Neural Networks,
MultMed(21), No. 8, August 2019, pp. 1971-1981.
IEEE DOI 1908
image classification, recurrent neural nets, multilabel image classification, recurrent neural networks, Recurrent Neural Network (RNN) BibRef

Chen, L.[Long], Wang, R.G.[Rong-Gui], Yang, J.[Juan], Xue, L.X.[Li-Xia], Hu, M.[Min],
Multi-label image classification with recurrently learning semantic dependencies,
VC(35), No. 10, October 2018, pp. 1361-1371.
WWW Link. 1909
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Xue, L.X.[Li-Xia], Jiang, D.[Di], Wang, R.G.[Rong-Gui], Yang, J.[Juan], Hu, M.[Min],
Learning semantic dependencies with channel correlation for multi-label classification,
VC(36), No. 7, July 2020, pp. 1325-1335.
WWW Link. 2005
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Huang, S.J.[Sheng-Jun], Gao, W.[Wei], Zhou1, Z.H.[Zhi-Hua],
Fast Multi-Instance Multi-Label Learning,
PAMI(41), No. 11, November 2019, pp. 2614-2627.
IEEE DOI 1910
Task analysis, Computational modeling, Semantics, Predictive models, Prediction algorithms, sub-concepts BibRef

Zhang, Y.[Yu], Wang, Y.[Yin], Liu, X.Y.[Xu-Ying], Mi, S.[Siya], Zhang, M.L.[Min-Ling],
Large-scale multi-label classification using unknown streaming images,
PR(99), 2020, pp. 107100.
Elsevier DOI 1912
Multi-label image classification, Recurrent novel-class detector, Streaming images BibRef

Vanegas, J.A.[Jorge A.], Beltrán, V.[Viviana], Escalante, H.J.[Hugo Jair], González, F.A.[Fabio A.],
Transductive non-linear semantic embedding for multi-class classification,
PRL(128), 2019, pp. 370-377.
Elsevier DOI 1912
Semantic representation, Semi-supervised learning, Transductive learning, Learning on a budget, Multi-class classification BibRef

Dai, Y.[Yong], Li, Y.[Yi], Li, S.T.[Shu-Tao],
Multi-label learning for concept-oriented labels of product image data,
IVC(93), 2020, pp. 103821.
Elsevier DOI 2001
Multi-label learning, Concept-oriented labels, Product image data, Feature correlation learning, Gram matrices BibRef

Xia, Y.X.[Yuan-Xin], d'Angelo, P.[Pablo], Tian, J.J.[Jiao-Jiao], Fraundorfer, F.[Friedrich], Reinartz, P.[Peter],
Multi-Label Learning based Semi-Global Matching Forest,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
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Yeh, M.C.[Mei-Chen], Li, Y.N.[Yi-Nan],
Multilabel Deep Visual-Semantic Embedding,
PAMI(42), No. 6, June 2020, pp. 1530-1536.
IEEE DOI 2005
Semantics, Computational modeling, Visualization, Training, Task analysis, Convolutional neural networks, Redundancy, convolutional neural networks BibRef

Hu, L.[Liang], Li, Y.H.[Yong-Hao], Gao, W.F.[Wan-Fu], Zhang, P.[Ping], Hu, J.C.[Jun-Cheng],
Multi-label feature selection with shared common mode,
PR(104), 2020, pp. 107344.
Elsevier DOI 2005
Feature selection, Multi-label learning, Coupled matrix factorization, Classification BibRef

Cevikalp, H.[Hakan], Benligiray, B.[Burak], Gerek, O.N.[Omer Nezih],
Semi-supervised robust deep neural networks for multi-label image classification,
PR(100), 2020, pp. 107164.
Elsevier DOI 2005
Multi-label classification, Semi-supervised learning, Ramp loss, Image classification, Deep learning BibRef

Zhu, P.P.[Pan-Pan], Tan, Y.M.[Yu-Min], Zhang, L.Q.[Li-Qiang], Wang, Y.B.[Yue-Bin], Mei, J.[Jie], Liu, H.[Hao], Wu, M.F.[Meng-Fan],
Deep Learning for Multilabel Remote Sensing Image Annotation With Dual-Level Semantic Concepts,
GeoRS(58), No. 6, June 2020, pp. 4047-4060.
IEEE DOI 2005
Attention mechanism, dual-level semantic concepts, remote sensing (RS) image multilabel annotation, triplet loss BibRef

Hua, Y.S.[Yuan-Sheng], Mou, L.C.[Li-Chao], Zhu, X.X.[Xiao Xiang],
Relation Network for Multilabel Aerial Image Classification,
GeoRS(58), No. 7, July 2020, pp. 4558-4572.
IEEE DOI 2006
Feature extraction, Semantics, Cognition, Correlation, Remote sensing, Task analysis, Soil, Attentional region extraction, multilabel classification BibRef

Ma, Z.C.[Zhong-Chen], Chen, S.C.[Song-Can],
Expand globally, shrink locally: Discriminant multi-label learning with missing labels,
PR(111), 2021, pp. 107675.
Elsevier DOI 2012
Multi-label learning, Missing labels, Local low-rank label structure, Label discriminant information BibRef

Lv, J.Q.[Jia-Qi], Wu, T.R.[Tian-Ran], Peng, C.L.[Cheng-Lun], Liu, Y.P.[Yun-Peng], Xu, N.[Ning], Geng, X.[Xin],
Compact learning for multi-label classification,
PR(113), 2021, pp. 107833.
Elsevier DOI 2103
Machine learning, Multi-label classification, Label compression, Compact learning BibRef

Quintanilla, E.[Erik], Rawat, Y.[Yogesh], Sakryukin, A.[Andrey], Shah, M.[Mubarak], Kankanhalli, M.[Mohan],
Adversarial Learning for Personalized Tag Recommendation,
MultMed(23), 2021, pp. 1083-1094.
IEEE DOI 2103
Visualization, Tagging, Deep learning, Convolutional neural networks, Encoding, adversarial learning BibRef

Yun, D.J.[Dong-Joo], Ryu, J.[Jongbin], Lim, J.W.[Jong-Woo],
Dual aggregated feature pyramid network for multi label classification,
PRL(144), 2021, pp. 75-81.
Elsevier DOI 2103
Deep learning, Multi-label classification, Aggregation BibRef

Xu, C.[Chao], Gu, S.L.[Shi-Lin], Tao, H.[Hong], Hou, C.P.[Chen-Ping],
Fragmentary label distribution learning via graph regularized maximum entropy criteria,
PRL(145), 2021, pp. 147-156.
Elsevier DOI 2104
Label distribution learning, Fragmentary label, Graph regularization, Label reconstruction BibRef

Xu, J.H.[Jia-Hao], Tian, H.D.[Hong-Da], Wang, Z.Y.[Zhi-Yong], Wang, Y.[Yang], Kang, W.X.[Wen-Xiong], Chen, F.[Fang],
Joint Input and Output Space Learning for Multi-Label Image Classification,
MultMed(23), 2021, pp. 1696-1707.
IEEE DOI 2106
Feature extraction, Correlation, Task analysis, Semantics, Deep learning, Visualization, Benchmark testing, deep learning BibRef

Chu, W.T.[Wei-Ta], Huang, S.H.[Si-Heng],
Multi-label image recognition by using semantics consistency, object correlation, and multiple samples,
JVCIR(77), 2021, pp. 103067.
Elsevier DOI 2106
Multi-label image recognition, Object correlation, Semantics consistency, Multiple samples BibRef

Sun, S.L.[Shi-Liang], Zong, D.M.[Dao-Ming],
LCBM: A Multi-View Probabilistic Model for Multi-Label Classification,
PAMI(43), No. 8, August 2021, pp. 2682-2696.
IEEE DOI 2107
Probabilistic logic, Task analysis, Prediction algorithms, Support vector machines, Kernel, Training, Semantics, variational autoencoder BibRef

Gao, B.B.[Bin-Bin], Zhou, H.Y.[Hong-Yu],
Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition,
IP(30), 2021, pp. 5920-5932.
IEEE DOI 2107
Streaming media, Image recognition, Semantics, Proposals, Visualization, Task analysis, Reinforcement learning, Multi-label, global to local BibRef

Song, G.L.[Guo-Li], Wang, S.H.[Shu-Hui], Huang, Q.M.[Qing-Ming], Tian, Q.[Qi],
Learning Feature Representation and Partial Correlation for Multimodal Multi-Label Data,
MultMed(23), 2021, pp. 1882-1894.
IEEE DOI 2107
Semantics, Correlation, Task analysis, Data models, Learning systems, Kernel, Deep learning, Cross-modal retrieval, correlation learning, partial correlation BibRef

Fan, Y.L.[Yu-Ling], Liu, J.H.[Jing-Hua], Liu, P.Z.[Pei-Zhong], Du, Y.Z.[Yong-Zhao], Lan, W.Y.[Wei-Yao], Wu, S.X.[Shun-Xiang],
Manifold learning with structured subspace for multi-label feature selection,
PR(120), 2021, pp. 108169.
Elsevier DOI 2109
Multi-label learning, Feature selection, Manifold learning, Structured subspace, Instance correlations, Label correlations BibRef

Zhang, P.[Ping], Liu, G.[Guixia], Gao, W.[Wanfu], Song, J.Z.[Jia-Zhi],
Multi-label feature selection considering label supplementation,
PR(120), 2021, pp. 108137.
Elsevier DOI 2109
Multi-label learning, Multi-label feature selection, Information theory, Label relationships BibRef

Zhang, M.L.[Min-Ling], Fang, J.P.[Jun-Peng],
Partial Multi-Label Learning via Credible Label Elicitation,
PAMI(43), No. 10, October 2021, pp. 3587-3599.
IEEE DOI 2109
Training, Computational complexity, Sensitivity analysis, Benchmark testing, Standards, Machine learning, credible label elicitation BibRef

Nakano, F.K.[Felipe Kenji], Pliakos, K.[Konstantinos], Vens, C.[Celine],
Deep tree-ensembles for multi-output prediction,
PR(121), 2022, pp. 108211.
Elsevier DOI 2109
Ensemble learning, Deep-forest, Multi-output prediction, Multi-target regression, Multi-label classification BibRef

Li, J.L.[Jun-Long], Li, P.P.[Pei-Pei], Hu, X.G.[Xue-Gang], Yu, K.[Kui],
Learning common and label-specific features for multi-Label classification with correlation information,
PR(121), 2022, pp. 108259.
Elsevier DOI 2109
Multi-label classification, Label-specific features, Common features, Instance correlation BibRef

Ma, X.K.[Xiao-Ke], Tan, S.Y.[Shi-Yin], Xie, X.H.[Xiang-Hua], Zhong, X.X.[Xiao-Xiong], Deng, J.J.[Jing-Jing],
Joint multi-label learning and feature extraction for temporal link prediction,
PR(121), 2022, pp. 108216.
Elsevier DOI 2109
Temporal link prediction, Non-negative matrix factorization, Multi-label learning, Dynamic networks BibRef

Guan, Y.Y.[Yuan-Yuan], Zhang, B.[Boxiang], Li, W.H.[Wen-Hui], Wang, Y.[Ying],
Semi-supervised partial multi-label classification with low-rank and manifold constraints,
PRL(151), 2021, pp. 112-119.
Elsevier DOI 2110
Multi-label classification, Semi-supervised, Partial multi-label BibRef

Wen, S.P.[Shi-Ping], Liu, W.W.[Wei-Wei], Yang, Y.[Yin], Zhou, P.[Pan], Guo, Z.Y.[Zhen-Yuan], Yan, Z.[Zheng], Chen, Y.[Yiran], Huang, T.W.[Ting-Wen],
Multilabel Image Classification via Feature/Label Co-Projection,
SMCS(51), No. 11, November 2021, pp. 7250-7259.
IEEE DOI 2110
Feature extraction, Correlation, Sports equipment, Visualization, Image recognition, Task analysis, Pipelines, Deep learning, neural network BibRef

Lin, D.[Dan], Lin, J.Z.[Jian-Zhe], Zhao, L.[Liang], Wang, Z.J.[Z. Jane], Chen, Z.K.[Zhi-Kui],
Multilabel Aerial Image Classification With a Concept Attention Graph Neural Network,
GeoRS(60), 2022, pp. 1-12.
IEEE DOI 2112
Correlation, Feature extraction, Task analysis, Semantics, Deep learning, Graph neural networks, Software, Aerial images, multilabel image classification BibRef

Nguyen, T.[Tam], Raich, R.[Raviv],
Incomplete Label Multiple Instance Multiple Label Learning,
PAMI(44), No. 3, March 2022, pp. 1320-1337.
IEEE DOI 2202
Labeling, Training, Phase locked loops, Birds, Numerical models, Graphical models, Standards, Incomplete-label learning, probabilistic models BibRef

Pei, Y.L.[Yuan-Li], Fern, X.L.[Xiao-Li], Raich, R.[Raviv],
Learning with Latent Label Hierarchy from Incomplete Multi-Label Data,
ICPR18(2075-2080)
IEEE DOI 1812
Labeling, Graphical models, Maximum likelihood estimation, Message passing, Probabilistic logic, Training, Dynamic programming BibRef

Sun, L.J.[Li-Juan], Feng, S.H.[Song-He], Liu, J.[Jun], Lyu, G.Y.[Geng-Yu], Lang, C.Y.[Cong-Yan],
Global-Local Label Correlation for Partial Multi-Label Learning,
MultMed(24), 2022, pp. 581-593.
IEEE DOI 2202
Correlation, Noise measurement, Predictive models, Matrix decomposition, Task analysis, Manifolds, Sparse matrices, label manifold BibRef

Shen, X.B.[Xiao-Bo], Dong, G.H.[Guo-Hua], Zheng, Y.[Yuhui], Lan, L.[Long], Tsang, I.W.[Ivor W.], Sun, Q.S.[Quan-Sen],
Deep Co-Image-Label Hashing for Multi-Label Image Retrieval,
MultMed(24), 2022, pp. 1116-1126.
IEEE DOI 2203
Codes, Semantics, Image retrieval, Hash functions, Prototypes, Quantization (signal), Hamming distance, Hashing, multi-label, image retrieval BibRef

Huang, S.[Shiluo], Liu, Z.[Zheng], Jin, W.[Wei], Mu, Y.[Ying],
Bag dissimilarity regularized multi-instance learning,
PR(126), 2022, pp. 108583.
Elsevier DOI 2204
Multi-instance learning (MIL), Dissimilarity regularization, Fisher score BibRef

Liu, S.[Siyu], Song, X.H.[Xue-Hua], Ma, Z.C.[Zhong-Chen], Ganaa, E.D.[Ernest Domanaanmwi], Shen, X.J.[Xiang-Jun],
MoRE: Multi-output residual embedding for multi-label classification,
PR(126), 2022, pp. 108584.
Elsevier DOI 2204
Distance metric, Low-rank structure, Residual embedding BibRef

Wang, Z.[Zhe], Fang, Z.[Zhongli], Li, D.D.[Dong-Dong], Yang, H.[Hai], Du, W.L.[Wen-Li],
Semantic Supplementary Network With Prior Information for Multi-Label Image Classification,
CirSysVideo(32), No. 4, April 2022, pp. 1848-1859.
IEEE DOI 2204
Semantics, Feature extraction, Predictive models, Image recognition, Task analysis, Sports, Deep learning, deep neural network BibRef

Ge, X.[Xiou], Wang, Y.C.[Yun-Cheng], Wang, B.[Bin], Kuo, C.C.J.[C.C. Jay],
CORE: A knowledge graph entity type prediction method via complex space regression and embedding,
PRL(157), 2022, pp. 97-103.
Elsevier DOI 2205
Knowledge graph, Complex space embedding, Entity type prediction, Self-adversarial negative samplin, Multi-label classification BibRef

Li, P.[Peng], Chen, P.[Peng], Zhang, D.Z.[De-Zheng],
Cross-Modal Feature Representation Learning and Label Graph Mining in a Residual Multi-Attentional CNN-LSTM Network for Multi-Label Aerial Scene Classification,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Xie, M.K.[Ming-Kun], Huang, S.J.[Sheng-Jun],
Partial Multi-Label Learning With Noisy Label Identification,
PAMI(44), No. 7, July 2022, pp. 3676-3687.
IEEE DOI 2206
Noise measurement, Task analysis, Training, Labeling, Crowdsourcing, Correlation, Phase locked loops, Multi-lable learning, multi-instance multi-label learning BibRef

Xie, M.K.[Ming-Kun], Huang, S.J.[Sheng-Jun],
CCMN: A General Framework for Learning With Class-Conditional Multi-Label Noise,
PAMI(45), No. 1, January 2023, pp. 154-166.
IEEE DOI 2212
Task analysis, Noise measurement, Training, Robustness, Labeling, Training data, Risk management, Class-conditional noise, partial multi-label learning BibRef

Zhou, F.T.[Feng-Tao], Huang, S.[Sheng], Liu, B.[Bo], Yang, D.[Dan],
Multi-Label Image Classification via Category Prototype Compositional Learning,
CirSysVideo(32), No. 7, July 2022, pp. 4513-4525.
IEEE DOI 2207
Semantics, Prototypes, Feature extraction, Task analysis, Convolutional neural networks, Computational modeling, Dogs, decomposing network BibRef

Li, D.[Dan], Du, C.D.[Chang-De], Wang, H.B.[Hai-Bao], Zhou, Q.Y.[Qiong-Yi], He, H.G.[Hui-Guang],
Deep Modality Assistance Co-Training Network for Semi-Supervised Multi-Label Semantic Decoding,
MultMed(24), 2022, pp. 3287-3299.
IEEE DOI 2207
Semantics, Task analysis, Decoding, Correlation, Generators, Face recognition, Training, Multi-label, Modality, Assistance, Semi-supervised BibRef

Zeng, H.Z.[Huan-Ze], Chen, A.[Argon],
Multivariate multi-layer classifier,
PR(131), 2022, pp. 108896.
Elsevier DOI 2208
Classification, Classifiers, Multivariate decision tree, Machine learning, Tree construction BibRef

Tan, A.[Anhui], Liang, J.[Jiye], Wu, W.Z.[Wei-Zhi], Zhang, J.[Jia],
Semi-Supervised Partial Multi-Label Classification via Consistency Learning,
PR(131), 2022, pp. 108839.
Elsevier DOI 2208
Semi-supervised partial multi-label learning, Label correlation, HSIC BibRef

Wang, J.[Jing], Geng, X.[Xin], Xue, H.[Hui],
Re-Weighting Large Margin Label Distribution Learning for Classification,
PAMI(44), No. 9, September 2022, pp. 5445-5459.
IEEE DOI 2208
Label Distribution Learning. Training, Support vector machines, Sun, Error probability, Predictive models, Faces, Computational modeling, generalization BibRef

Liu, W.W.[Wei-Wei], Wang, H.[Haobo], Shen, X.B.[Xiao-Bo], Tsang, I.W.[Ivor W.],
The Emerging Trends of Multi-Label Learning,
PAMI(44), No. 11, November 2022, pp. 7955-7974.
IEEE DOI 2210
Deep learning, Task analysis, Market research, Training, Testing, Noise measurement, Correlation, Extreme multi-label learning, new applications BibRef

Wang, D.B.[Deng-Bao], Zhang, M.L.[Min-Ling], Li, L.[Li],
Adaptive Graph Guided Disambiguation for Partial Label Learning,
PAMI(44), No. 12, December 2022, pp. 8796-8811.
IEEE DOI 2212
Training, Labeling, Predictive models, Task analysis, Manifolds, Faces, Tagging, Machine learning, weakly supervised learning, label disambiguation BibRef

Wu, H.J.[Hong-Jun], Xu, C.[Cheng], Liu, H.Z.[Hong-Zhe],
SMART: Semantic-Aware Masked Attention Relational Transformer for Multi-label Image Recognition,
SPLetters(29), 2022, pp. 2158-2162.
IEEE DOI 2212
Transformers, Task analysis, Image recognition, Semantics, Correlation, Convolution, Visualization, masked attention BibRef

Li, Y.H.[Yong-Hao], Hu, L.[Liang], Gao, W.[Wanfu],
Multi-label feature selection via robust flexible sparse regularization,
PR(134), 2023, pp. 109074.
Elsevier DOI 2212
Multi-label learning, Feature selection, Sparse regularization, Classification BibRef

Li, Y.H.[Yong-Hao], Hu, L.[Liang], Gao, W.[Wanfu],
Robust sparse and low-redundancy multi-label feature selection with dynamic local and global structure preservation,
PR(134), 2023, pp. 109120.
Elsevier DOI 2212
Feature selection, Multi-label learning, Sparse learning, Label correlations BibRef

Gao, W.[Wanfu], Hao, P.T.[Ping-Ting], Wu, Y.[Yang], Zhang, P.[Ping],
A unified low-order information-theoretic feature selection framework for multi-label learning,
PR(134), 2023, pp. 109111.
Elsevier DOI 2212
Feature selection, Multi-label learning, Information theory, Low-order information-theoretic terms, Probability distribution assumption BibRef

Maltoudoglou, L.[Lysimachos], Paisios, A.[Andreas], Lenc, L.[Ladislav], Martínek, J.[Jirí], Král, P.[Pavel], Papadopoulos, H.[Harris],
Well-calibrated confidence measures for multi-label text classification with a large number of labels,
PR(122), 2022, pp. 108271.
Elsevier DOI 2112
Text classification, Multi-label, Word2vec, Bert, Conformal prediction, Label powerset, Confidence measure BibRef

Zhai, T.T.[Ting-Ting], Wang, H.[Hao], Tang, H.C.[Hong-Cheng],
Joint optimization of scoring and thresholding models for online multi-label classification,
PR(136), 2023, pp. 109167.
Elsevier DOI 2301
online multi-label classification, online thresholding, adaptive thresholding, online learning BibRef

Qian, K.[Kun], Min, X.Y.[Xue-Yang], Cheng, Y.S.[Yu-Sheng], Min, F.[Fan],
Weight matrix sharing for multi-label learning,
PR(136), 2023, pp. 109156.
Elsevier DOI 2301
Low-rank, Missing labels, Multi-label learning, Shared weight, Sparse BibRef

Zhou, W.[Wei], Dou, P.[Peng], Su, T.[Tao], Hu, H.F.[Hai-Feng], Zheng, Z.J.[Zhi-Jie],
Feature learning network with transformer for multi-label image classification,
PR(136), 2023, pp. 109203.
Elsevier DOI 2301
Multi-label classification, Transformer, Multi-scale features, Spatial attention, Salient features, Feature suppression BibRef

Wang, Y.[Yun], Zhang, T.[Tong], Zhou, C.[Chuanwei], Cui, Z.[Zhen], Yang, J.[Jian],
Instance-Aware Deep Graph Learning for Multi-Label Classification,
MultMed(25), 2023, pp. 90-99.
IEEE DOI 2301
Correlation, Adaptation models, Task analysis, Feature extraction, Image recognition, Convolutional neural networks, Sports, variational inference BibRef

Stoimchev, M.[Marjan], Kocev, D.[Dragi], Džeroski, S.[Sašo],
Deep Network Architectures as Feature Extractors for Multi-Label Classification of Remote Sensing Images,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Yazici, V.O.[Vacit Oguz], van de Weijer, J.[Joost], Yu, L.L.[Long-Long],
Visual Transformers with Primal Object Queries for Multi-Label Image Classification,
ICPR22(3014-3020)
IEEE DOI 2212
Training, Measurement, Visualization, Object detection, Transformers, Decoding BibRef

Zhou, D.H.[Dong-Hao], Chen, P.F.[Peng-Fei], Wang, Q.[Qiong], Chen, G.Y.[Guang-Yong], Heng, P.A.[Pheng-Ann],
Acknowledging the Unknown for Multi-Label Learning with Single Positive Labels,
ECCV22(XXIV:423-440).
Springer DOI 2211
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Ke, B.[Bo], Zhu, Y.Q.[Yun-Quan], Li, M.[Mengtian], Shu, X.J.[Xiu-Jun], Qiao, R.Z.[Rui-Zhi], Ren, B.[Bo],
Hyperspherical Learning in Multi-Label Classification,
ECCV22(XXV:38-55).
Springer DOI 2211
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Lin, D.[Dan], Chen, Z.K.[Zhi-Kui], Zhao, L.[Liang], Wang, K.[Kai],
Multi-label Aerial Image Classification Based on Image-Specific Concept Graphs,
ICIP22(121-125)
IEEE DOI 2211
Computational modeling, Image edge detection, Semantics, Image representation, Feature extraction, Task analysis, Graph convolutional network BibRef

Lan, Z.[Ziwen], Maeda, K.[Keisuke], Ogawa, T.[Takahiro], Haseyama, M.[Miki],
GCN-Based Multi-Modal Multi-Label Attribute Classification in Anime Illustration Using Domain-Specific Semantic Features,
ICIP22(2021-2025)
IEEE DOI 2211
Semantics, Production, Animation, Task analysis, Anime illustration, graph convolutional networks, semantic feature, image captioning BibRef

Dong, N.Q.[Nan-Qing], Wang, J.Y.[Jia-Yi], Voiculescu, I.[Irina],
Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity,
L3D-IVU22(4211-4219)
IEEE DOI 2210
Bridges, Costs, Supervised learning, Entropy, Data models BibRef

Kim, Y.[Youngwook], Kim, J.M.[Jae Myung], Akata, Z.[Zeynep], Lee, J.[Jungwoo],
Large Loss Matters in Weakly Supervised Multi-Label Classification,
CVPR22(14136-14145)
IEEE DOI 2210
Training, Costs, Codes, Annotations, Computational modeling, Pattern recognition, Recognition: detection, categorization, Self- semi- meta- unsupervised learning BibRef

Ben-Baruch, E.[Emanuel], Ridnik, T.[Tal], Friedman, I.[Itamar], Ben-Cohen, A.[Avi], Zamir, N.[Nadav], Noy, A.[Asaf], Zelnik-Manor, L.[Lihi],
Multi-label Classification with Partial Annotations using Class-aware Selective Loss,
CVPR22(4754-4762)
IEEE DOI 2210
Training, Deep learning, Codes, Annotations, Computational modeling, Estimation, Recognition: detection, categorization, retrieval, Machine learning BibRef

Ben-Cohen, A.[Avi], Zamir, N.[Nadav], Baruch, E.B.[Emanuel Ben], Friedman, I.[Itamar], Zelnik-Manor, L.[Lihi],
Semantic Diversity Learning for Zero-Shot Multi-label Classification,
ICCV21(620-630)
IEEE DOI 2203
Training, Image recognition, Computational modeling, Semantics, Image retrieval, Neural networks, Recognition and classification, Representation learning BibRef

Hu, S.[Shu], Ke, L.[Lipeng], Wang, X.[Xin], Lyu, S.W.[Si-Wei],
TkML-AP: Adversarial Attacks to Top-k Multi-Label Learning,
ICCV21(7629-7637)
IEEE DOI 2203
Learning systems, Text analysis, Perturbation methods, Semantics, Image annotation, Dogs, Benchmark testing, Adversarial learning, BibRef

Narayan, S.[Sanath], Gupta, A.[Akshita], Khan, S.[Salman], Khan, F.S.[Fahad Shahbaz], Shao, L.[Ling], Shah, M.[Mubarak],
Discriminative Region-based Multi-Label Zero-Shot Learning,
ICCV21(8711-8720)
IEEE DOI 2203
Visualization, Codes, Semantics, Benchmark testing, Cognition, Spatial resolution, Recognition and classification BibRef

Zhu, K.[Ke], Wu, J.X.[Jian-Xin],
Residual Attention: A Simple but Effective Method for Multi-Label Recognition,
ICCV21(184-193)
IEEE DOI 2203
Training, Visualization, Image recognition, Codes, Computational modeling, Task analysis, BibRef

Chen, M.[Ming], Wang, G.[Guijin], Xue, J.H.[Jing-Hao], Ding, Z.J.[Zi-Jian], Sun, L.[Li],
Enhance Via Decoupling: Improving Multi-Label Classifiers With Variational Feature Augmentation,
ICIP21(1329-1333)
IEEE DOI 2201
Deep learning, Visualization, Image recognition, Benchmark testing, Network architecture, Feature extraction, Deep Learning, Pedestrian Attribute Recognition BibRef

Guo, H.[Hao], Wang, S.[Song],
Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-balanced Samplings,
CVPR21(15084-15093)
IEEE DOI 2111
Training, Visualization, Head, Collaboration, Collaborative work, Pattern recognition BibRef

Lanchantin, J.[Jack], Wang, T.L.[Tian-Lu], Ordonez, V.[Vicente], Qi, Y.J.[Yan-Jun],
General Multi-label Image Classification with Transformers,
CVPR21(16473-16483)
IEEE DOI 2111
Training, Deep learning, Visualization, Genomics, Transformers, Encoding, Pattern recognition BibRef

Magri, L.[Luca], Leveni, F.[Filippo], Boracchi, G.[Giacomo],
MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection,
CVPR21(1853-1862)
IEEE DOI 2111
Couplings, Codes, Fitting, Clustering algorithms, Tools, Data models BibRef

Song, Z.[Zeyu], Chang, D.L.[Dong-Liang], Ma, Z.Y.[Zhan-Yu], Li, X.X.[Xiao-Xu], Tan, Z.H.[Zheng-Hua],
CC-Loss: Channel Correlation Loss for Image Classification,
ICPR21(7601-7608)
IEEE DOI 2105
Training, Deep learning, Correlation, Euclidean distance, Feature extraction, Entropy, Deep Learning, Channel Attention BibRef

Li, Y.N.[Ya-Ning], Yang, L.[Liu],
More Correlations Better Performance: Fully Associative Networks for Multi-label Image Classification,
ICPR21(9437-9444)
IEEE DOI 2105
Fans, Visualization, Correlation, Image recognition, Convolution, Predictive models, Image representation BibRef

Koßmann, D.[Dominik], Wilhelm, T.[Thorsten], Fink, G.A.[Gernot A.],
Towards Tackling Multi-Label Imbalances in Remote Sensing Imagery,
ICPR21(5782-5789)
IEEE DOI 2105
Measurement, Earth, Image analysis, Computational modeling, Neural networks, Employment, Encoding, Scene understanding BibRef

Campos, L.S.[Leonardo S.], Salvadeo, D.H.P.[Denis H. P.],
Multi-label Classification of Panoramic Radiographic Images Using a Convolutional Neural Network,
ISVC20(I:346-358).
Springer DOI 2103
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Maeda, K.[Keisuke], Takahashi, S.[Sho], Ogawa, T.[Takahiro], Haseyama, M.[Miki],
Feature Integration Via Geometrical Supervised Multi-View Multi-Label Canonical Correlation For Incomplete Label Assignment,
ICIP20(46-50)
IEEE DOI 2011
Correlation, Linear programming, Training, Covariance matrices, Visualization, Semantics, Task analysis, Incomplete label, canonical correlation. BibRef

Ma, J.H.[Jiang-Hong], Liu, Y.[Yang],
Latent Topic-aware Multi-label Classification,
ECCV20(XIV:558-573).
Springer DOI 2011
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Li, J.B.[Jun-Bing], Zhang, C.Q.[Chang-Qing], Zhu, P.F.[Peng-Fei], Wu, B.Y.[Bao-Yuan], Chen, L.[Lei], Hu, Q.H.[Qing-Hua],
SPL-MLL: Selecting Predictable Landmarks for Multi-label Learning,
ECCV20(IX:783-799).
Springer DOI 2011
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Wu, T.[Tong], Huang, Q.Q.[Qing-Qiu], Liu, Z.W.[Zi-Wei], Wang, Y.[Yu], Lin, D.[Dahua],
Distribution-balanced Loss for Multi-label Classification in Long-tailed Datasets,
ECCV20(IV:162-178).
Springer DOI 2011
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Oguz Yazici, V., Gonzalez-Garcia, A., Ramisa, A., Twardowski, B., van de Weijer, J.,
Orderless Recurrent Models for Multi-Label Classification,
CVPR20(13437-13446)
IEEE DOI 2008
Task analysis, Training, Predictive models, Computational modeling, Logic gates, Correlation, Computer architecture BibRef

Rajat, Varshney, M., Singh, P., Namboodiri, V.P.,
Minimizing Supervision in Multi-label Categorization,
TCV20(93-102)
IEEE DOI 2008
Measurement, Task analysis, Machine learning, Welding, Feature extraction, Uncertainty, Computational modeling BibRef

Huynh, D., Elhamifar, E.,
Interactive Multi-Label CNN Learning With Partial Labels,
CVPR20(9420-9429)
IEEE DOI 2008
Training, Loss measurement, Training data, Manifolds, Memory management, Image recognition, Standards BibRef

Guan, Z.Q.[Zi-Qiao], Yager, K.G.[Kevin G.], Yu, D.T.[Dan-Tong], Qin, H.[Hong],
Multi-Label Visual Feature Learning with Attentional Aggregation,
WACV20(2190-2198)
IEEE DOI 2006
identifying weak scattered patterns with diffuse background interference. X-ray scattering, Active appearance model, Visualization, Imaging, Training, X-ray imaging, Detectors BibRef

Trajdos, P.[Pawel], Kurzynski, M.[Marek],
Dynamic Classifier Chains for Multi-label Learning,
GCPR19(567-580).
Springer DOI 1911
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Wang, Q., Jia, N., Breckon, T.P.,
A Baseline for Multi-Label Image Classification Using an Ensemble of Deep Convolutional Neural Networks,
ICIP19(644-648)
IEEE DOI 1910
Multi-Label Image Classification, Deep Convolutional Neural Network, Data Augmentation BibRef

Chen, Z., Lin, J., Wang, Z., Chandrasekhar, V., Lin, W.,
Beyond Ranking Loss: Deep Holographic Networks for Multi-Label Video Search,
ICIP19(879-883)
IEEE DOI 1910
Video Search, Multi-label, Deep Metric Learning, Feature Composition BibRef

Zhang, M.Y.[Meng-Ying], Li, C.S.[Chang-Sheng], Wang, X.F.[Xiang-Feng],
Multi-View Metric Learning for Multi-Label Image Classification,
ICIP19(2134-2138)
IEEE DOI 1910
Multi-label image classification, distance metric learning, multi-view learning BibRef

Jiang, B., Zhou, L., Lin, L., Xu, B., Yu, J., Zheng, X., Wu, K.,
A Real-Time Multi-Label Classification System for Short Videos,
ICIP19(534-538)
IEEE DOI 1910
Short-video analysis, multi-label classification, deep learning, real-time inference BibRef

Guo, B., Hou, C., Shan, J., Yi, D.,
Low Rank Multi-Label Classification with Missing Labels,
ICPR18(417-422)
IEEE DOI 1812
Correlation, Optimization, Semantics, Linear programming, Matrices, Data models, Training BibRef

Firman, M.[Michael], Campbell, N.D.F.[Neill D. F.], Agapito, L.[Lourdes], Brostow, G.J.[Gabriel J.],
DiverseNet: When One Right Answer is not Enough,
CVPR18(5598-5607)
IEEE DOI 1812
Training, Task analysis, Aerospace electronics, Supervised learning, Training data BibRef

Chu, H.M.[Hong-Min], Yeh, C.K.[Chih-Kuan], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Deep Generative Models for Weakly-Supervised Multi-Label Classification,
ECCV18(II: 409-425).
Springer DOI 1810
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Hong, D.F.[Dan-Feng], Yokoya, N.[Naoto], Xu, J.[Jian], Zhu, X.X.[Xiao-Xiang],
Joint and Progressive Learning from High-Dimensional Data for Multi-label Classification,
ECCV18(VIII: 478-493).
Springer DOI 1810
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Raimundo, M.M.[Marcos M.], von Zuben, F.J.[Fernando J.],
Many-Objective Ensemble-Based Multilabel Classification,
CIARP17(365-373).
Springer DOI 1802
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Xie, P., Salakhutdinov, R., Mou, L., Xing, E.P.,
Deep Determinantal Point Process for Large-Scale Multi-label Classification,
ICCV17(473-482)
IEEE DOI 1802
image classification, learning (artificial intelligence), neural nets, Deep Determinantal Point Process model, MLC, Visualization BibRef

Ghosh, A.[Aritra], Sekhar, C.C.[C. Chandra],
Label Correlation Propagation for Semi-supervised Multi-label Learning,
PReMI17(52-60).
Springer DOI 1711
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Singh, A.K.[Abhiram Kumar], Sekhar, C.C.[C. Chandra],
A Two-Stage Conditional Random Field Model Based Framework for Multi-Label Classification,
PReMI17(69-76).
Springer DOI 1711
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Yang, H., Zhou, J.T., Cai, J., Ong, Y.S.,
MIML-FCN+: Multi-Instance Multi-Label Learning via Fully Convolutional Networks with Privileged Information,
CVPR17(5996-6004)
IEEE DOI 1711
Correlation, Image recognition, Machine learning, Proposals, Training BibRef

Zhu, F.[Feng], Li, H.S.[Hong-Sheng], Ouyang, W.L.[Wan-Li], Yu, N.H.[Neng-Hai], Wang, X.G.[Xiao-Gang],
Learning Spatial Regularization with Image-Level Supervisions for Multi-label Image Classification,
CVPR17(2027-2036)
IEEE DOI 1711
Kernel, Neural networks, Semantics, Training, Visualization BibRef

Kimura, K., Kudo, M., Sun, L.[Lu], Koujaku, S.,
Fast random k-labELsets for large-scale multi-label classification,
ICPR16(438-443)
IEEE DOI 1705
Algorithm design and analysis, Complexity theory, Computational modeling, Support vector machines, Testing, Training BibRef

Guo, Y.M.[Yu-Meng], Chung, F.L.[Fu-Lai], Li, G.Z.[Guo-Zheng],
Multi-label learning with globAl densiTy fusiOn Mapping features,
ICPR16(462-467)
IEEE DOI 1705
Algorithm design and analysis, Clustering algorithms, Image reconstruction, Prediction algorithms, Space exploration, Training, Uniform, resource, locators BibRef

Sun, L., Kudo, M., Kimura, K.,
Multi-label classification with meta-label-specific features,
ICPR16(1612-1617)
IEEE DOI 1705
Complexity theory, Correlation, Feature extraction, Prediction algorithms, Semantics, Sparse matrices, Text, categorization BibRef

Norov-Erdene, B., Kudo, M., Sun, L., Kimura, K.,
Locality in multi-label classification problems,
ICPR16(2319-2324)
IEEE DOI 1705
Biology, Clustering algorithms, Entropy, Pattern recognition, Regression tree analysis, Testing, Training BibRef

Kanehira, A., Shin, A., Harada, T.,
True-negative label selection for large-scale multi-label learning,
ICPR16(3673-3678)
IEEE DOI 1705
Computational modeling, Convergence, Dogs, Fasteners, Optimization, Training BibRef

Li, C.H.[Cheng-Hua], Kang, Q.[Qi], Ge, G.J.[Guo-Jing], Song, Q.[Qiang], Lu, H.Q.[Han-Qing], Cheng, J.[Jian],
DeepBE: Learning Deep Binary Encoding for Multi-label Classification,
ChaLearn16(744-751)
IEEE DOI 1612
BibRef

Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.,
CNN-RNN: A Unified Framework for Multi-label Image Classification,
CVPR16(2285-2294)
IEEE DOI 1612
BibRef

Yang, H.[Hao], Zhou, J.T.Y.[Joey Tian-Yi], Cai, J.F.[Jian-Fei],
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations,
ECCV16(I: 835-851).
Springer DOI 1611
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Kimura, K.[Keigo], Kudo, M.[Mineichi], Sun, L.[Lu],
Simultaneous Nonlinear Label-Instance Embedding for Multi-label Classification,
SSSPR16(15-25).
Springer DOI 1611
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Kwok, J.T.,
Learning from High-Dimensional Data in Multitask/Multilabel Classification,
ACPR13(16-17)
IEEE DOI 1408
learning (artificial intelligence) BibRef

Sun, F.M.[Fu-Ming], Xu, M.X.[Mei-Xiang], Jiang, X.J.[Xiao-Jun],
Robust Multi-Label Image Classification with Semi-Supervised Learning and Active Learning,
MMMod15(II: 512-523).
Springer DOI 1501
BibRef

Sechidis, K.[Konstantinos], Nikolaou, N.[Nikolaos], Brown, G.[Gavin],
Information Theoretic Feature Selection in Multi-label Data through Composite Likelihood,
SSSPR14(143-152).
Springer DOI 1408
BibRef

Zhang, L.F.[Ling-Feng], Shah, S.K.[Shishir K.], Kakadiaris, I.A.[Ioannis A.],
Hierarchical Multi-label Classification using Fully Associative Ensemble Learning,
PR(70), No. 1, 2017, pp. 89-103.
Elsevier DOI 1706
BibRef
Earlier:
Fully Associative Ensemble Learning for Hierarchical Multi-Label Classification,
BMVC14(xx-yy).
HTML Version. 1410
Hierarchical Multi-label BibRef

Reyes-Pupo, O.G.[Oscar Gabriel], Morell, C.[Carlos], Ventura-Soto, S.[Sebastián],
ReliefF-ML: An Extension of ReliefF Algorithm to Multi-label Learning,
CIARP13(II:528-535).
Springer DOI 1311
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Cai, X.[Xiao], Nie, F.P.[Fei-Ping], Cai, W.D.[Wei-Dong], Huang, H.[Heng],
New Graph Structured Sparsity Model for Multi-label Image Annotations,
ICCV13(801-808)
IEEE DOI 1403
Graph Structured Sparsity BibRef

Kong, D.G.[De-Guang], Ding, C.[Chris], Huang, H.[Heng], Zhao, H.F.[Hai-Feng],
Multi-label ReliefF and F-statistic feature selections for image annotation,
CVPR12(2352-2359).
IEEE DOI 1208
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Pang, Y.W.[Yan-Wei], Ma, Z.[Zhao], Yuan, Y.[Yuan], Li, X.L.[Xue-Long], Wang, K.Q.[Kong-Qiao],
Multimodal learning for multi-label image classification,
ICIP11(1797-1800).
IEEE DOI 1201
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Nowak, S.[Stefanie], Llorente, A.[Ainhoa], Motta, E.[Enrico], Rüger, S.[Stefan],
The effect of semantic relatedness measures on multi-label classification evaluation,
CIVR10(303-310).
DOI Link 1007
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Classifier, Performance Evaluation, Errors, Comparisons .