14.1.8.8 Multi-Task Learning, Multiple Tasks, Transfer Learning, Domain Adaption

Zhang, X.,
Convex Discriminative Multitask Clustering,
PAMI(37), No. 1, January 2015, pp. 28-40.
IEEE DOI 1412
Bismuth BibRef

Huang, P.P.[Pi-Pei], Wang, G.[Gang], Qin, S.Y.[Shi-Yin],
A novel learning approach to multiple tasks based on boosting methodology,
PRL(31), No. 12, 1 September 2010, pp. 1693-1700.
Elsevier DOI 1008
Boosting; Multi-task learning; Inductive transfer learning; Multiple tasks; Text classification BibRef

Huang, P.P.[Pi-Pei], Wang, G.[Gang], Qin, S.Y.[Shi-Yin],
Boosting for transfer learning from multiple data sources,
PRL(33), No. 5, 1 April 2012, pp. 568-579.
Elsevier DOI 1202
Opinion mining; Sentimental classification; Boosting; Transfer learning; Transfer learning with multiple sources; Multiple source domains BibRef

Zhang, Z.H.[Zhi-Hao], Zhou, J.[Jie],
Multi-task clustering via domain adaptation,
PR(45), No. 1, 2012, pp. 465-473.
Elsevier DOI 1410
Multi-task clustering BibRef

Chen, J.H.[Jian-Hui], Tang, L.[Lei], Liu, J.[Jun], Ye, J.P.[Jie-Ping],
A Convex Formulation for Learning a Shared Predictive Structure from Multiple Tasks,
PAMI(35), No. 5, May 2013, pp. 1025-1038.
IEEE DOI 1304
BibRef

Li, W.[Wen], Duan, L.X.[Li-Xin], Xu, D.[Dong], Tsang, I.W.H.[Ivor Wai-Hung],
Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation,
PAMI(36), No. 6, June 2014, pp. 1134-1148.
IEEE DOI 1406
BibRef
Earlier: A1, A2, A4, A3:
Batch mode Adaptive Multiple Instance Learning for computer vision tasks,
CVPR12(2368-2375).
IEEE DOI 1208
Convergence BibRef

Xu, X.X.[Xin-Xing], Li, W.[Wen], Xu, D.[Dong], Tsang, I.W.H.[Ivor Wai-Hung],
Co-Labeling for Multi-View Weakly Labeled Learning,
PAMI(38), No. 6, June 2016, pp. 1113-1125.
IEEE DOI 1605
Kernel.
See also Image Classification With Densely Sampled Image Windows and Generalized Adaptive Multiple Kernel Learning. BibRef

Chen, X.[Xu], Chen, S.[Siheng], Yao, J.C.[Jiang-Chao], Zheng, H.J.[Huang-Jie], Zhang, Y.[Ya], Tsang, I.W.[Ivor W.],
Learning on Attribute-Missing Graphs,
PAMI(44), No. 2, February 2022, pp. 740-757.
IEEE DOI 2201
Task analysis, Generative adversarial networks, Convolution, Recurrent neural networks, Linear programming, link prediction BibRef

Wang, D.H.[Dong-Hui], Kong, S.[Shu],
A classification-oriented dictionary learning model: Explicitly learning the particularity and commonality across categories,
PR(47), No. 2, 2014, pp. 885-898.
Elsevier DOI 1311
BibRef
Earlier: A2, A1:
Transfer heterogeneous unlabeled data for unsupervised clustering,
ICPR12(1193-1196).
WWW Link. 1302
BibRef
Earlier: A2, A1:
A multi-task learning strategy for unsupervised clustering via explicitly separating the commonality,
ICPR12(771-774).
WWW Link. 1302
Dictionary learning BibRef

Abdulnabi, A.H., Wang, G., Lu, J., Jia, K.,
Multi-Task CNN Model for Attribute Prediction,
MultMed(17), No. 11, November 2015, pp. 1949-1959.
IEEE DOI 1511
Clothing BibRef

Fan, J.P.[Jian-Ping], Zhao, T.Y.[Tian-Yi], Kuang, Z.Z.[Zhen-Zhong], Zheng, Y.[Yu], Zhang, J.[Ji], Yu, J.[Jun], Peng, J.Y.[Jin-Ye],
HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition,
IP(26), No. 4, April 2017, pp. 1923-1938.
IEEE DOI 1704
Atomic layer deposition. Large-scale. BibRef

Liu, T., Tao, D., Song, M., Maybank, S.J.,
Algorithm-Dependent Generalization Bounds for Multi-Task Learning,
PAMI(39), No. 2, February 2017, pp. 227-241.
IEEE DOI 1702
Algorithm design and analysis BibRef

Zhang, Y.X.[Yu-Xiang], Wu, K.[Ke], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Hu, X.Y.[Xiang-Yun],
Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706

See also Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection. BibRef

Dong, Y.N.[Yan-Ni], Zhang, L.P.[Liang-Pei], Zhang, L.F.[Le-Fei], Du, B.[Bo],
Maximum Margin Metric Learning Based Target Detection for Hyperspectral Images,
PandRS(108), No. 1, 2015, pp. 138-150.
Elsevier DOI 1511
Target detection BibRef

Dong, Y.N.[Yan-Ni], Shi, W.Z.[Wen-Zhong], Du, B.[Bo], Hu, X.Y.[Xiang-Yun], Zhang, L.P.[Liang-Pei],
Asymmetric Weighted Logistic Metric Learning for Hyperspectral Target Detection,
Cyber(52), No. 10, October 2022, pp. 11093-11106.
IEEE DOI 2209
Object detection, Measurement, Hyperspectral imaging, Training, Logistics, Task analysis, Linear programming, target detection BibRef

Dong, Y.N.[Yan-Ni], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Hu, X.Y.[Xiang-Yun],
Hyperspectral Target Detection via Adaptive Information-Theoretic Metric Learning with Local Constraints,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Ghifary, M.[Muhammad], Balduzzi, D.[David], Kleijn, W.B.[W. Bastiaan], Zhang, M.J.[Meng-Jie],
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization,
PAMI(39), No. 7, July 2017, pp. 1414-1430.
IEEE DOI 1706
BibRef
Earlier: A1, A3, A2, A4:
Domain Generalization for Object Recognition with Multi-task Autoencoders,
ICCV15(2551-2559)
IEEE DOI 1602
Algorithm design and analysis, Kernel, Object recognition, Optimization, Standards, Training, Visualization, Domain adaptation, domain generalization, feature learning, kernel methods, object recognition, scatter BibRef

Ghifary, M.[Muhammad], Kleijn, W.B.[W. Bastiaan], Zhang, M.J.[Meng-Jie], Balduzzi, D.[David], Li, W.[Wen],
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation,
ECCV16(IV: 597-613).
Springer DOI 1611
Feature extraction BibRef

Kuang, Z.Z.[Zhen-Zhong], Yu, J.[Jun], Li, Z.M.[Zong-Min], Zhang, B.P.[Bao-Peng], Fan, J.P.[Jian-Ping],
Integrating multi-level deep learning and concept ontology for large-scale visual recognition,
PR(78), 2018, pp. 198 - 214.
Elsevier DOI 1804
Large-scale visual recognition, Multi-level deep learning, Multiple deep networks, Concept ontology, Multi-task learning, Tree classifier BibRef

Li, P.[Ping], Chen, S.C.[Song-Can],
Hierarchical Gaussian Processes model for multi-task learning,
PR(74), No. 1, 2018, pp. 134-144.
Elsevier DOI 1711
GP-LVM BibRef

Li, P.[Ping], Chen, S.C.[Song-Can],
Gaussian process approach for metric learning,
PR(87), 2019, pp. 17-28.
Elsevier DOI 1812
Metric learning, Gaussian process, Bilinear similarity, Non-parametric metric BibRef

Xu, W.[Wei], Liu, W.[Wei], Chi, H.Y.[Hao-Yuan], Huang, X.L.[Xiao-Lin], Yang, J.[Jie],
Multi-task classification with sequential instances and tasks,
SP:IC(64), 2018, pp. 59-67.
Elsevier DOI 1804
Classification, Multi-task learning, Curriculum learning, Self-paced learning BibRef

Sarafianos, N.[Nikolaos], Giannakopoulos, T.[Theodoros], Nikou, C.[Christophoros], Kakadiaris, I.A.[Ioannis A.],
Curriculum learning of visual attribute clusters for multi-task classification,
PR(80), 2018, pp. 94-108.
Elsevier DOI 1805
BibRef
Earlier:
Curriculum Learning for Multi-task Classification of Visual Attributes,
TASKCV17(2608-2615)
IEEE DOI 1802
Curriculum learning, Multi-task classification. Visual attribute classification. Learn individual groups of tasks. Correlation, Feature extraction, Machine learning, Training, Visualization BibRef

Cao, W.M.[Wen-Ming], Qian, S.[Sheng], Wu, S.[Si], Wong, H.S.[Hau-San],
Unsupervised Multi-task Learning with Hierarchical Data Structure,
PR(86), 2019, pp. 248-264.
Elsevier DOI 1811
Multi-task learning, hierarchical structure, unsupervised learning, structural similarity, BibRef

Mejjati, Y.A., Cosker, D., Kim, K.I.,
Multi-task Learning by Maximizing Statistical Dependence,
CVPR18(3465-3473)
IEEE DOI 1812
Task analysis, Random variables, Kernel, Mutual information, Gaussian processes, Probability distribution, Classification algorithms BibRef

Li, X.H.[Xu-Hong], Grandvalet, Y.[Yves], Davoine, F.[Franck], Cheng, J.C.[Jing-Chun], Cui, Y.[Yin], Zhang, H.[Hang], Belongie, S.[Serge], Tsai, Y.H.[Yi-Hsuan], Yang, M.H.[Ming-Hsuan],
Transfer learning in computer vision tasks: Remember where you come from,
IVC(93), 2020, pp. 103853.
Elsevier DOI 2001
Transfer learning, Parameter regularization BibRef

Liu, D.[Deyin], Liu, L.C.[Liang-Chen], Tie, Y.[Yun], Qi, L.[Lin],
Multi-task image set classification via joint representation with class-level sparsity and intra-task low-rankness,
PRL(132), 2020, pp. 99-105.
Elsevier DOI 2005
Multi-task recognition, Image set classification, Class-level sparsity, Low-rankness BibRef

He, H.X.[Han-Xian], Khoshelham, K.[Kourosh], Fraser, C.[Clive],
A multiclass TrAdaBoost transfer learning algorithm for the classification of mobile lidar data,
PandRS(166), 2020, pp. 118-127.
Elsevier DOI 2007
To apply deep learnign to LiDAR data. Not enough training data. VoxNet, TrAdaBoost, Multiclass classification, Point Cloud, 3DCNN, Deep learning, Transfer learning BibRef

Adiyeke, E.[Esra], Baydogan, M.G.[Mustafa Gökçe],
The benefits of target relations: A comparison of multitask extensions and classifier chains,
PR(107), 2020, pp. 107507.
Elsevier DOI 2008
Multitask learning, Multi-objective trees, Stacking, Classifier chains, Ensemble learning BibRef

Yang, P.[Pei], Tan, Q.[Qi], He, J.R.[Jing-Rui],
Complex heterogeneity learning: A theoretical and empirical study,
PR(107), 2020, pp. 107519.
Elsevier DOI 2008
Heterogeneous learning, Multi-task learning, Multi-view learning, Multi-instance learning BibRef

Yang, H.W.[Hong-Wei], He, H.[Hui], Li, T.[Tao], Bai, Y.W.[Ya-Wen], Zhang, W.Z.[Wei-Zhe],
Multi-metric domain adaptation for unsupervised transfer learning,
IET-IPR(14), No. 12, October 2020, pp. 2780-2790.
DOI Link 2010
BibRef

Zhao, Z.C.[Zhi-Cheng], Luo, Z.[Ze], Li, J.[Jian], Chen, C.[Can], Piao, Y.C.[Ying-Chao],
When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on a Multitask Learning Framework,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Sun, X.W.[Xin-Wei], Xu, Y.L.[Yi-Lun], Cao, P.[Peng], Kong, Y.Q.[Yu-Qing], Hu, L.J.[Ling-Jing], Zhang, S.H.[Shang-Hang], Wang, Y.Z.[Yi-Zhou],
TCGM: An Information-theoretic Framework for Semi-supervised Multi-modality Learning,
ECCV20(III:171-188).
Springer DOI 2012
BibRef

Alvar, S.R., Bajic, I.V.,
Pareto-Optimal Bit Allocation for Collaborative Intelligence,
IP(30), 2021, pp. 3348-3361.
IEEE DOI 2103
BibRef
Earlier:
Multi-Task Learning with Compressible Features for Collaborative Intelligence,
ICIP19(1705-1709)
IEEE DOI 1910
Tensors, Task analysis, Image coding, Bit rate, Nonlinear distortion, Distortion measurement, Optimization, Bit allocation, multi-task learning. collaborative intelligence, deep feature compression BibRef

Zhang, R.[Rui], Zhang, H.Y.[Hong-Yuan], Li, X.L.[Xue-Long],
Robust Multi-Task Learning With Flexible Manifold Constraint,
PAMI(43), No. 6, June 2021, pp. 2150-2157.
IEEE DOI 2106
Task analysis, Manifolds, Adaptation models, Robustness, Training, Computational modeling, Predictive models, Multi-task learning, regression BibRef

Chang, W.[Wei], Nie, F.P.[Fei-Ping], Wang, R.[Rong], Li, X.L.[Xue-Long],
Elaborate multi-task subspace learning with discrete group constraint,
PR(139), 2023, pp. 109515.
Elsevier DOI 2304
Multi-task learning, Negative transfer, Subspace learning, Re-weighted method BibRef

Li, J.[Jie], Huang, L.[Lei], Wei, Z.Q.[Zhi-Qiang], Zhang, W.F.[Wen-Feng], Qin, Q.B.[Qi-Bing],
Multi-task learning with deformable convolution,
JVCIR(77), 2021, pp. 103109.
Elsevier DOI 2106
Multi-task learning, Deformable convolution, Recognition BibRef

Tomar, D.[Devavrat], Lortkipanidze, M.[Manana], Vray, G.[Guillaume], Bozorgtabar, B.[Behzad], Thiran, J.P.[Jean-Philippe],
Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation,
MedImg(40), No. 10, October 2021, pp. 2926-2938.
IEEE DOI 2110
Image segmentation, Biomedical imaging, Computed tomography, Magnetic resonance imaging, Semantics, Anatomical structure, self-attention BibRef

Yang, Z.Y.[Zhi-Yong], Xu, Q.Q.[Qian-Qian], Cao, X.C.[Xiao-Chun], Huang, Q.M.[Qing-Ming],
Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction,
PAMI(43), No. 11, November 2021, pp. 4094-4110.
IEEE DOI 2110
Task analysis, Convergence, Predictive models, Diseases, Collaborative work, Optimization, Training, global convergence BibRef

Adiyeke, E.[Esra], Baydogan, M.G.[Mustafa Gökçe],
Semi-supervised extensions of multi-task tree ensembles,
PR(123), 2022, pp. 108393.
Elsevier DOI 2112
Semi-supervised learning, Multi-task learning, Multi-objective trees, Ensemble learning, Totally randomized trees BibRef

Neogi, S.[Satyajit], Dauwels, J.[Justin],
Factored Latent-Dynamic Conditional Random Fields for single and multi-label sequence modeling,
PR(122), 2022, pp. 108236.
Elsevier DOI 2112
Conditional Random Fields, Sequence labeling, Multi-task learning, Latent-Dynamic models, Probabilistic graphical models BibRef

Liang, J.[Jian], Liu, Z.Q.[Zi-Qi], Zhou, J.Y.[Jia-Yu], Jiang, X.Q.[Xiao-Qian], Zhang, C.S.[Chang-Shui], Wang, F.[Fei],
Model-Protected Multi-Task Learning,
PAMI(44), No. 2, February 2022, pp. 1002-1019.
IEEE DOI 2201
Task analysis, Covariance matrices, Privacy, Security, Data models, Resource management, Multi-task learning, model protection, low-rank subspace learning BibRef

Zhou, Y.[Yu], Li, X.[Xiaoni], Zhou, Y.[Yucan], Wang, Y.[Yu], Hu, Q.H.[Qing-Hua], Wang, W.P.[Wei-Ping],
Deep Collaborative Multi-Task Network: A Human Decision Process Inspired Model for Hierarchical Image Classification,
PR(124), 2022, pp. 108449.
Elsevier DOI 2203
Hierarchical image classification, Deep multi-task network, Collaborative learning, Decision uncertainty evaluation BibRef

Cui, C.R.[Chao-Ran], Shen, Z.[Zhen], Huang, J.[Jin], Chen, M.[Meng], Xu, M.L.[Ming-Liang], Wang, M.[Meng], Yin, Y.L.[Yi-Long],
Adaptive Feature Aggregation in Deep Multi-Task Convolutional Neural Networks,
CirSysVideo(32), No. 4, April 2022, pp. 2133-2144.
IEEE DOI 2204
Task analysis, Training, Visualization, Convolution, Residual neural networks, Feature extraction, attention mechanism BibRef

Liu, J.B.[Jia-Bin], Qi, Z.Q.[Zhi-Quan], Wang, B.[Bo], Tian, Y.J.[Ying-Jie], Shi, Y.[Yong],
SELF-LLP: Self-supervised learning from label proportions with self-ensemble,
PR(129), 2022, pp. 108767.
Elsevier DOI 2206
Learning from label proportion, Self-supervised learning, Self-ensemble strategy, Multi-task learning BibRef

Zhu, Y.[Yi], Wu, X.D.[Xin-Dong], Qiang, J.P.[Ji-Peng], Hu, X.G.[Xue-Gang], Zhang, Y.H.[Yu-Hong], Li, P.P.[Pei-Pei],
Representation learning with deep sparse auto-encoder for multi-task learning,
PR(129), 2022, pp. 108742.
Elsevier DOI 2206
Deep sparse auto-encoder, Multi-task learning, RICA, Labeled and unlabeled data BibRef

Vandenhende, S.[Simon], Georgoulis, S.[Stamatios], van Gansbeke, W.[Wouter], Proesmans, M.[Marc], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Multi-Task Learning for Dense Prediction Tasks: A Survey,
PAMI(44), No. 7, July 2022, pp. 3614-3633.
IEEE DOI 2206
Survey, Multi-Task Learning. Task analysis, Deep learning, Optimization, Neural networks, Taxonomy, Multi-task learning, convolutional neural networks BibRef

Lu, Y.[Yuwu], Zhu, Q.[Qi], Zhang, B.[Bob], Lai, Z.H.[Zhi-Hui], Li, X.L.[Xue-Long],
Weighted Correlation Embedding Learning for Domain Adaptation,
IP(31), 2022, pp. 5303-5316.
IEEE DOI 2208
Task analysis, Correlation, Transfer learning, Image classification, Feature extraction, Measurement, image classification BibRef

Zhang, H.[Hong], Li, Y.[Yang], Yang, H.Q.[Han-Qing], He, B.[Bin], Zhang, Y.[Yu],
Isomorphic model-based initialization for convolutional neural networks,
JVCIR(89), 2022, pp. 103677.
Elsevier DOI 2212
Model family: collection of related networks. Convolutional neural networks, Weight initialization, Isomorphic model, Structural weight transformation BibRef

Strezoski, G.[Gjorgji], van Noord, N.[Nanne], Worring, M.[Marcel],
MATTE: Multi-task multi-scale attention,
CVIU(228), 2023, pp. 103622.
Elsevier DOI 2302
BibRef
Earlier:
Many Task Learning With Task Routing,
ICCV19(1375-1384)
IEEE DOI 2004
Multi-task learning, Matting, Visual Decathlon, Ubernet. learning (artificial intelligence), pattern classification, conditional feature-wise transformation, classification tasks, Adaptation models BibRef

Zhang, R.[Rui], Yang, Y.X.[Yi-Xin], Li, Y.[Yang], Wang, J.[Jiabao], Li, H.[Hang], Miao, Z.[Zhuang],
Multi-task few-shot learning with composed data augmentation for image classification,
IET-CV(17), No. 2, 2023, pp. 211-221.
DOI Link 2304
data augmentation, few-shot learning, multi-task learning, non-maximum suppression, self-supervised learning BibRef

Aghajanzadeh, E.[Emad], Bahraini, T.[Tahereh], Mehrizi, A.H.[Amir Hossein], Yazdi, H.S.[Hadi Sadoghi],
Task weighting based on particle filter in deep multi-task learning with a view to uncertainty and performance,
PR(140), 2023, pp. 109587.
Elsevier DOI 2305
Multi task learning, Uncertainty, Hyper-parameter tuning, Deep learning, Particle filter, Bayesian estimation BibRef

Meng, M.[Min], Lan, M.C.[Meng-Cheng], Yu, J.[Jun], Wu, J.G.[Ji-Gang], Liu, L.G.[Li-Gang],
Dual-Level Adaptive and Discriminative Knowledge Transfer for Cross-Domain Recognition,
MultMed(25), 2023, pp. 2266-2279.
IEEE DOI 2306
Kernel, Adaptation models, Risk management, Task analysis, Knowledge transfer, Visualization, Transforms, structural risk minimization BibRef

Zhang, X.Y.[Xiao-Ya], Zhang, S.M.[Shu-Min], Cui, Z.[Zhen], Li, Z.C.[Ze-Chao], Xie, J.[Jin], Yang, J.[Jian],
Tube-Embedded Transformer for Pixel Prediction,
MultMed(25), 2023, pp. 2503-2514.
IEEE DOI 2307
Task analysis, Multitasking, Estimation, Electron tubes, Decoding, Semantics, Learning systems, Depth estimation, tube-embedded transforme BibRef

Guo, T.[Tan], Luo, F.[Fulin], Duan, Y.[Yule], Huang, X.J.[Xin-Jian], Shi, G.Y.[Guang-Yao],
Rethinking Representation Learning-Based Hyperspectral Target Detection: A Hierarchical Representation Residual Feature-Based Method,
RS(15), No. 14, 2023, pp. 3608.
DOI Link 2307
Learning-based hyperspectral target detection. BibRef

Fang, Y.C.[Yu-Chun], Cai, S.[Sirui], Cao, Y.T.[Yi-Ting], Li, Z.C.[Zheng-Chen], Zhang, Z.X.[Zhao-Xiang],
Adversarial Learning Guided Task Relatedness Refinement for Multi-Task Deep Learning,
MultMed(25), 2023, pp. 6946-6957.
IEEE DOI 2311
BibRef

Yan, X.Q.[Xiao-Qiang], Mao, Y.Q.[Yi-Qiao], Li, M.Y.[Ming-Yuan], Ye, Y.D.[Yang-Dong], Yu, H.[Hui],
Multitask Image Clustering via Deep Information Bottleneck,
Cyber(54), No. 3, March 2024, pp. 1868-1881.
IEEE DOI 2402
Task analysis, Correlation, Feature extraction, Optimization, Mutual information, Cybernetics, Training, Image clustering, mutual information (MI) BibRef

Mao, Y.Q.[Yi-Qiao], Yan, X.Q.[Xiao-Qiang], Liu, J.[JiaMing], Ye, Y.D.[Yang-Dong],
ConGMC: Consistency-Guided Multimodal Clustering via Mutual Information Maximin,
MultMed(26), 2024, pp. 5131-5146.
IEEE DOI 2404
Mutual information, Task analysis, Clustering methods, Head, Representation learning, Kernel, Correlation, superfluous information BibRef

Yu, H.H.[Hui-Hui], Dai, Q.[Qun],
Self-supervised multi-task learning for medical image analysis,
PR(150), 2024, pp. 110327.
Elsevier DOI 2403
Medical image analysis, Self-supervised multi-task learning, Uniformity regularization, Chest X-ray image BibRef

Lu, Y.Y.[Yuan-Yuan], Zhu, Y.H.[Yan-Hui], Feng, H.[Hao], Liu, Y.[Yang],
Remote Sensing Scene Classification Using Multi-Domain Sematic High-Order Network,
IVC(143), 2024, pp. 104948.
Elsevier DOI 2403
Remote sensing, Scene classification, Convolutional neural networks, Deep semantic feature, Second-order BibRef

Li, W.H.[Wei-Hong], Liu, X.L.[Xia-Lei], Bilen, H.[Hakan],
Universal Representations: A Unified Look at Multiple Task and Domain Learning,
IJCV(132), No. 5, May 2024, pp. 1521-1545.
Springer DOI 2405
BibRef

Xin, Z.W.[Ze-Wei], Sirejiding, S.[Shalayiding], Lu, Y.X.[Yu-Xiang], Ding, Y.[Yue], Wang, C.L.[Chun-Lin], Alsarhan, T.[Tamam], Lu, H.T.[Hong-Tao],
TFUT: Task fusion upward transformer model for multi-task learning on dense prediction,
CVIU(244), 2024, pp. 104014.
Elsevier DOI 2405
Vision transformer, Multi-task learning, Dense prediction BibRef

Nie, X.[Xing], Ni, B.[Bolin], Chang, J.L.[Jian-Long], Meng, G.F.[Gao-Feng], Huo, C.L.[Chun-Lei], Xiang, S.M.[Shi-Ming], Tian, Q.[Qi],
Pro-Tuning: Unified Prompt Tuning for Vision Tasks,
CirSysVideo(34), No. 6, June 2024, pp. 4653-4667.
IEEE DOI 2406
Task analysis, Adaptation models, Tuning, Computational modeling, Transformers, Visualization, Training, Prompt-based learning, transfer learning BibRef

Fontana, M.[Maxime], Spratling, M.[Michael], Shi, M.J.[Miao-Jing],
When Multitask Learning Meets Partial Supervision: A Computer Vision Review,
PIEEE(112), No. 6, June 2024, pp. 516-543.
IEEE DOI Code:
WWW Link. 2409
Reviews, Optimization methods, Sparse matrices, Natural language processing, Computational modeling, visual understanding BibRef

Wu, G.J.[Gao-Jie], Zeng, L.A.[Ling-An], Meng, J.K.[Jing-Ke], Zheng, W.S.[Wei-Shi],
Adaptive Weight Generator for Multi-Task Image Recognition by Task Grouping Prompt,
MultMed(26), 2024, pp. 9906-9919.
IEEE DOI 2410
Task analysis, Multitasking, Adaptation models, Generators, Image recognition, Feature extraction, Computational modeling, weight generator BibRef

Xie, T.[Tao], Dai, K.[Kun], Sun, Q.H.[Qi-Hao], Jiang, Z.Q.[Zhi-Qiang], Cao, C.Q.[Chu-Qing], Zhao, L.J.[Li-Jun], Wang, K.[Ke], Li, R.F.[Rui-Feng],
CO-Net++: A Cohesive Network for Multiple Point Cloud Tasks at Once With Two-Stage Feature Rectification,
PAMI(46), No. 12, December 2024, pp. 10911-10928.
IEEE DOI 2411
Task analysis, Point cloud compression, Optimization, Feature extraction, Sun, Surgery, Deep learning, multi-task learning, two-stage feature rectification BibRef

Xie, T.[Tao], Wang, K.[Ke], Lu, S.[Siyi], Zhang, Y.K.[Yu-Kun], Dai, K.[Kun], Li, X.Y.[Xiao-Yu], Xu, J.[Jie], Wang, L.[Li], Zhao, L.J.[Li-Jun], Zhang, X.Y.[Xin-Yu], Li, R.F.[Rui-Feng],
CO-Net: Learning Multiple Point Cloud Tasks at Once with A Cohesive Network,
ICCV23(3500-3510)
IEEE DOI 2401
BibRef

Ye, H.R.[Han-Rong], Xu, D.[Dan],
InvPT++: Inverted Pyramid Multi-Task Transformer for Visual Scene Understanding,
PAMI(46), No. 12, December 2024, pp. 7493-7508.
IEEE DOI 2411
BibRef
Earlier:
Inverted Pyramid Multi-task Transformer for Dense Scene Understanding,
ECCV22(XXVII:514-530).
Springer DOI 2211
Task analysis, Multitasking, Transformers, Decoding, Computational modeling, Visualization, Context modeling, transformer BibRef

Sirejiding, S.[Shalayiding], Bayramli, B.[Bayram], Lu, Y.X.[Yu-Xiang], Huang, S.[Suizhi], Lu, H.T.[Hong-Tao], Ding, Y.[Yue],
Adaptive Task-Wise Message Passing for Multi-Task Learning: A Spatial Interaction Perspective,
CirSysVideo(34), No. 10, October 2024, pp. 9499-9514.
IEEE DOI 2411
Task analysis, Decoding, Multitasking, Message passing, Adaptation models, Feature extraction, Estimation, graph neural network BibRef

Li, M.X.[Mei-Xuan], Li, T.Y.[Tian-Yu], Wang, G.Q.[Guo-Qing], Wang, P.[Peng], Yang, Y.[Yang], Zou, J.[Jie],
Region-aware Distribution Contrast: A Novel Approach to Multi-task Partially Supervised Learning,
ECCV24(LI: 234-251).
Springer DOI 2412
BibRef

Zhang, R.Y.[Rui-Yuan], Chen, Y.[Yuyao], Liu, J.X.[Jia-Xiang], Xi, D.B.[Dian-Bing], Huo, Y.[Yuchi], Liu, J.[Jie], Wu, C.[Chao],
SGW-Based Multi-task Learning in Vision Tasks,
ACCV24(IV: 124-141).
Springer DOI 2412
BibRef

Li, W.[Wenyi], Gao, H.A.[Huan-Ang], Gao, M.J.[Ming-Ju], Tian, B.[Beiwen], Zhi, R.[Rong], Zhao, H.[Hao],
Training-free Model Merging for Multi-target Domain Adaptation,
ECCV24(XLVII: 419-438).
Springer DOI 2412
BibRef

Srivastava, S.[Siddharth], Sharma, G.[Gaurav],
OmniVec2: A Novel Transformer Based Network for Large Scale Multimodal and Multitask Learning,
CVPR24(27402-27414)
IEEE DOI 2410
Training, Point cloud compression, Knowledge engineering, Head, Time series analysis, Computer architecture, Switches BibRef

Yang, Y.Q.[Yu-Qi], Jiang, P.T.[Peng-Tao], Hou, Q.[Qibin], Zhang, H.[Hao], Chen, J.[Jinwei], Li, B.[Bo],
Multi-Task Dense Prediction via Mixture of Low-Rank Experts,
CVPR24(27927-27937)
IEEE DOI Code:
WWW Link. 2410
Measurement, Learning systems, Convolution, Computational modeling, Predictive models, Multitasking, Multi-task learning, Low-rank structure BibRef

Huang, H.M.[Hui-Min], Huang, Y.W.[Ya-Wen], Lin, L.[Lanfen], Tong, R.F.[Ruo-Feng], Chen, Y.W.[Yen-Wei], Zheng, H.[Hao], Yue-Xiang, L., Zheng, Y.F.[Ye-Feng],
Going Beyond Multi-Task Dense Prediction with Synergy Embedding Models,
CVPR24(28181-28190)
IEEE DOI 2410
Visualization, Adaptation models, Predictive models, Benchmark testing, Multitasking, Transformers, Prediction algorithms BibRef

Marza, P.[Pierre], Matignon, L.[Laetitia], Simonin, O.[Olivier], Wolf, C.[Christian],
Task-Conditioned Adaptation of Visual Features in Multi-Task Policy Learning,
CVPR24(17847-17856)
IEEE DOI 2410
Training, Visualization, Decision making, Visual systems, Benchmark testing, Multitasking, Embodied AI, Adaptation, Few-shot learning BibRef

Nishi, K.[Kento], Kim, J.[Junsik], Li, W.[Wanhua], Pfister, H.[Hanspeter],
Joint-Task Regularization for Partially Labeled Multi-Task Learning,
CVPR24(16152-16162)
IEEE DOI 2410
Training, Learning systems, Machine learning, Benchmark testing, Multitasking, Multi-Task Learning, Cross-Task Regularization BibRef

Xu, Z.Q.[Zheng-Qi], Yuan, K.[Ke], Wang, H.Q.[Hui-Qiong], Wang, Y.[Yong], Song, M.L.[Ming-Li], Song, J.[Jie],
Training-Free Pretrained Model Merging,
CVPR24(5915-5925)
IEEE DOI Code:
WWW Link. 2410
Training, Adaptation models, Visualization, Computational modeling, Merging, Computer architecture, Multitasking, model merging, multi-task BibRef

Singh, I.P.[Inder Pal], Ghorbel, E.[Enjie], Kacem, A.[Anis], Rathinam, A.[Arunkumar], Aouada, D.[Djamila],
Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification,
WACV24(3924-3933)
IEEE DOI 2404
Training, Codes, Fitting, Gaussian distribution, Task analysis, Gaussian mixture model, Algorithms, Adversarial learning, Image recognition and understanding BibRef

Bhattacharjee, D.[Deblina], Süsstrunk, S.[Sabine], Salzmann, M.[Mathieu],
Vision Transformer Adapters for Generalizable Multitask Learning,
ICCV23(18969-18980)
IEEE DOI Code:
WWW Link. 2401
BibRef

Shi, H.[Haosen], Ren, S.[Shen], Zhang, T.W.[Tian-Wei], Pan, S.J.L.[Sinno Jia-Lin],
Deep Multitask Learning with Progressive Parameter Sharing,
ICCV23(19867-19878)
IEEE DOI 2401
BibRef

Zhang, J.[Ji], Gao, L.L.[Lian-Li], Luo, X.[Xu], Shen, H.T.[Heng-Tao], Song, J.K.[Jing-Kuan],
DETA: Denoised Task Adaptation for Few-Shot Learning,
ICCV23(11507-11517)
IEEE DOI Code:
WWW Link. 2401
BibRef

Huang, Z.J.[Zhi-Jian], Lin, S.[Sihao], Liu, G.Y.[Gui-Yu], Luo, M.[Mukun], Ye, C.Q.[Chao-Qiang], Xu, H.[Hang], Chang, X.J.[Xiao-Jun], Liang, X.D.[Xiao-Dan],
FULLER: Unified Multi-modality Multi-task 3D Perception via Multi-level Gradient Calibration,
ICCV23(3479-3488)
IEEE DOI 2401
BibRef

Aich, A.[Abhishek], Schulter, S.[Samuel], Roy-Chowdhury, A.K.[Amit K.], Chandraker, M.[Manmohan], Suh, Y.M.[Yu-Min],
Efficient Controllable Multi-Task Architectures,
ICCV23(5717-5728)
IEEE DOI 2401
BibRef

Yun, H.Y.[Ha-Young], Cho, H.[Hanjoo],
Achievement-based Training Progress Balancing for Multi-Task Learning,
ICCV23(16889-16898)
IEEE DOI Code:
WWW Link. 2401
BibRef

Shoouri, S.[Sara], Yang, M.Y.[Ming-Yu], Fan, Z.C.[Zi-Chen], Kim, H.S.[Hun-Seok],
Efficient Computation Sharing for Multi-Task Visual Scene Understanding,
ICCV23(17084-17095)
IEEE DOI Code:
WWW Link. 2401
BibRef

Lê, H.Â.[Hoàng-Ân], Pham, M.T.[Minh-Tan],
Self-training and multi-task learning for limited data: Evaluation study on object detection,
LIMIT23(1003-1009)
IEEE DOI Code:
WWW Link. 2401
BibRef

Sarker, N.H.[Najibul Haque], Rahman, M.S.[M. Sohel],
Forward Diffusion Guided Reconstruction as a Multi-Modal Multi-Task Learning Scheme,
ICIP23(3180-3184)
IEEE DOI 2312
BibRef

Srivastava, S.[Siddharth], Bhugra, S.[Swati], Kaushik, V.[Vinay], Lall, B.[Brejesh],
Hierarchical Multi-task Learning via Task Affinity Groupings,
ICIP23(3289-3293)
IEEE DOI 2312
BibRef

Chen, S.W.[Si-Wei], Ma, X.[Xiao], Xu, Z.W.[Zhong-Wen],
Imitation Learning as State Matching via Differentiable Physics,
CVPR23(7846-7855)
IEEE DOI 2309
BibRef

Fostiropoulos, I.[Iordanis], Zhu, J.[Jiaye], Itti, L.[Laurent],
Batch Model Consolidation: A Multi-Task Model Consolidation Framework,
CVPR23(3664-3676)
IEEE DOI 2309
BibRef

Choi, W.[Wonhyeok], Im, S.H.[Sung-Hoon],
Dynamic Neural Network for Multi-Task Learning Searching across Diverse Network Topologies,
CVPR23(3779-3788)
IEEE DOI 2309
BibRef

Liu, Y.J.[Ya-Jing], Lu, Y.N.[Yu-Ning], Liu, H.[Hao], An, Y.Z.[Yao-Zu], Xu, Z.R.[Zhuo-Ran], Yao, Z.K.[Zhuo-Kun], Zhang, B.F.[Bao-Feng], Xiong, Z.W.[Zhi-Wei], Gui, C.G.[Chen-Guang],
Hierarchical Prompt Learning for Multi-Task Learning,
CVPR23(10888-10898)
IEEE DOI 2309
BibRef

Chen, Z.[Zitian], Shen, Y.[Yikang], Ding, M.Y.[Ming-Yu], Chen, Z.F.[Zhen-Fang], Zhao, H.S.[Heng-Shuang], Learned-Miller, E.[Erik], Gan, C.[Chuang],
Mod-Squad: Designing Mixtures of Experts As Modular Multi-Task Learners,
CVPR23(11828-11837)
IEEE DOI 2309
BibRef

Senushkin, D.[Dmitry], Patakin, N.[Nikolay], Kuznetsov, A.[Arseny], Konushin, A.[Anton],
Independent Component Alignment for Multi-Task Learning,
CVPR23(20083-20093)
IEEE DOI 2309
BibRef

Rahimian, E.[Elahe], Javadi, G.[Golara], Tung, F.[Frederick], Oliveira, G.[Gabriel],
DynaShare: Task and Instance Conditioned Parameter Sharing for Multi-Task Learning,
ECV23(4535-4543)
IEEE DOI 2309
BibRef

Neseem, M.[Marina], Agiza, A.[Ahmed], Reda, S.[Sherief],
AdaMTL: Adaptive Input-dependent Inference for Efficient Multi-Task Learning,
ECV23(4730-4739)
IEEE DOI 2309
BibRef

Ding, C.T.[Chun-Tao], Lu, Z.C.[Zhi-Chao], Wang, S.G.[Shang-Guang], Cheng, R.[Ran], Boddeti, V.N.[Vishnu N.],
Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives,
CVPR23(7756-7765)
IEEE DOI 2309
BibRef

Oh, C.[Changdae], Hwang, H.[Hyeji], Lee, H.Y.[Hee-Young], Lim, Y.T.[Yong-Taek], Jung, G.[Geunyoung], Jung, J.Y.[Ji-Young], Choi, H.[Hosik], Song, K.[Kyungwoo],
BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning,
CVPR23(24224-24235)
IEEE DOI 2309
BibRef

Sohn, K.[Kihyuk], Chang, H.[Huiwen], Lezama, J.[José], Polania, L.[Luisa], Zhang, H.[Han], Hao, Y.[Yuan], Essa, I.[Irfan], Jiang, L.[Lu],
Visual Prompt Tuning for Generative Transfer Learning,
CVPR23(19840-19851)
IEEE DOI 2309
BibRef

Lin, S.[Shen], Zhang, X.Y.[Xiao-Yu], Chen, C.Y.[Chen-Yang], Chen, X.F.[Xiao-Feng], Susilo, W.[Willy],
ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer,
CVPR23(20147-20155)
IEEE DOI 2309
BibRef

Fan, C.Y.[Cao-Yun], Chen, W.Q.[Wen-Qing], Tian, J.[Jidong], Li, Y.T.[Yi-Tian], He, H.[Hao], Jin, Y.[Yaohui],
MAXGNR: A Dynamic Weight Strategy via Maximizing Gradient-to-noise Ratio for Multi-Task Learning,
ACCV22(I:523-538).
Springer DOI 2307
BibRef

Su, S.[Stephen], Kwong, S.[Samuel], Zhao, Q.Y.[Qing-Yu], Huang, D.A.[De-An], Niebles, J.C.[Juan Carlos], Adeli, E.[Ehsan],
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding,
DSC22(317-333).
Springer DOI 2304
BibRef

Berlier, A.J.[Adam J.], Bhatt, A.[Anjali], Matuszek, C.[Cynthia],
Augmenting Simulation Data with Sensor Effects for Improved Domain Transfer,
ACVR22(765-779).
Springer DOI 2304
BibRef

Susladkar, O.[Onkar], Deshmukh, G.[Gayatri], Makwana, D.[Dhruv], Mittal, S.[Sparsh], Teja, R.S.C.[R. Sai Chandra], Singhal, R.[Rekha],
GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal downstream tasks,
WACV23(5231-5240)
IEEE DOI 2302
Deep learning, Visualization, Image synthesis, Grounding, Semantics, Bidirectional control, visual reasoning BibRef

Heidemann, L.[Lena], Monnet, M.[Maureen], Roscher, K.[Karsten],
Concept Correlation and Its Effects on Concept-Based Models,
WACV23(4769-4777)
IEEE DOI 2302
Location awareness, Correlation, Shape, Computational modeling, Predictive models, Robustness, Algorithms: Explainable, fair, visual reasoning BibRef

Lopes, I.[Ivan], Vu, T.H.[Tuan-Hung], de Charette, R.[Raoul],
Cross-task Attention Mechanism for Dense Multi-task Learning,
WACV23(2328-2337)
IEEE DOI 2302
Representation learning, Geometry, Semantic segmentation, Semantics, Estimation, visual reasoning BibRef

Fu, Q.S.[Qing-Shun], Dong, X.[Xuan],
Semi-Supervised Depth Estimation by Multi-Task Learning,
ICPR22(3765-3771)
IEEE DOI 2212
Semantic segmentation, Estimation, Predictive models, Multitasking, Natural language processing, Decoding BibRef

Sinodinos, D.[Dimitrios], Armanfard, N.[Narges],
Attentive Task Interaction Network for Multi-Task Learning,
ICPR22(2885-2891)
IEEE DOI 2212
Knowledge engineering, Measurement, Learning systems, Semantic segmentation, Estimation, Feature extraction, Multitasking BibRef

Pranavan, T.[Theivendiram], Sim, T.[Terence], Li, J.S.[Jian-Shu],
Virtual Tasks but Real Gains: Improving Multi-Task Learning,
ICPR22(4829-4836)
IEEE DOI 2212
Machine learning, Performance gain, Gain measurement, Multitasking, Task analysis BibRef

Zhu, H.Y.[Hao-Yi], Wang, C.T.[Chu-Ting], Wang, Y.X.[Yuan-Xin], Fan, Z.X.[Zhao-Xin], Uddin, M.R.[Mostofa Rafid], Gao, X.[Xin], Zhang, J.[Jing], Zeng, X.[Xiangrui], Xu, M.[Min],
Unsupervised Multi-Task Learning for 3D Subtomogram Image Alignment, Clustering and Segmentation,
ICIP22(2751-2755)
IEEE DOI 2211
Training, Image segmentation, Image recognition, Benchmark testing, Tomography, Network architecture, subtomogram segmentation BibRef

Xu, X.G.[Xiao-Gang], Zhao, H.S.[Heng-Shuang], Vineet, V.[Vibhav], Lim, S.N.[Ser-Nam], Torralba, A.[Antonio],
MTFormer: Multi-task Learning via Transformer and Cross-Task Reasoning,
ECCV22(XXVII:304-321).
Springer DOI 2211
BibRef

Bouniot, Q.[Quentin], Loesch, A.[Angélique], Habrard, A.[Amaury], Audigier, R.[Romaric],
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient?,
WACV23(75-84)
IEEE DOI 2302
Deformable models, Adaptation models, Education, Object detection, Detectors, Predictive models, Transformers, visual reasoning BibRef

Bouniot, Q.[Quentin], Redko, I.[Ievgen], Audigier, R.[Romaric], Loesch, A.[Angélique], Habrard, A.[Amaury],
Improving Few-Shot Learning Through Multi-task Representation Learning Theory,
ECCV22(XX:435-452).
Springer DOI 2211
BibRef

Li, W.[Wanhua], Cao, Z.[Zhexuan], Feng, J.J.[Jian-Jiang], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
Label2Label: A Language Modeling Framework for Multi-attribute Learning,
ECCV22(XII:562-579).
Springer DOI 2211
BibRef

Kim, S.[Sunkyung], Choi, H.[Hyesong], Min, D.B.[Dong-Bo],
Sequential Cross Attention Based Multi-Task Learning,
ICIP22(2311-2315)
IEEE DOI 2211
Visualization, Image segmentation, Codes, Aggregates, Estimation, Multitasking, Feature extraction, Multi-task learning, monocular depth estimation BibRef

Rivera, C.G.[Corban G.], Handelman, D.A.[David A.], Ratto, C.R.[Christopher R.], Patrone, D.[David], Paulhamus, B.L.[Bart L.],
Visual Goal-Directed Meta-Imitation Learning,
CLVision22(3766-3772)
IEEE DOI 2210
Visualization, Benchmark testing, Manipulators, Trajectory, Planning BibRef

Spencer, J.[Jaime], Bowden, R.[Richard], Hadfield, S.[Simon],
Medusa: Universal Feature Learning via Attentional Multitasking,
CLVision22(3799-3808)
IEEE DOI 2210
Representation learning, Couplings, Multitasking, Decoding BibRef

Li, W.H.[Wei-Hong], Liu, X.L.[Xia-Lei], Bilen, H.[Hakan],
Learning Multiple Dense Prediction Tasks from Partially Annotated Data,
CVPR22(18857-18867)
IEEE DOI 2210
Training, Semisupervised learning, Benchmark testing, Multitasking, Computational efficiency, Self- semi- meta- Scene analysis and understanding BibRef

Yang, L.[Li], Rakin, A.S.[Adnan Siraj], Fan, D.L.[De-Liang],
DA3: Dynamic Additive Attention Adaption for Memory-Efficient On-Device Multi-Domain Learning,
ECV22(2618-2626)
IEEE DOI 2210
Training, Performance evaluation, Adaptation models, Visualization, Costs, Additives, Computational modeling BibRef

Rebut, J.[Julien], Ouaknine, A.[Arthur], Malik, W.[Waqas], Pérez, P.[Patrick],
Raw High-Definition Radar for Multi-Task Learning,
CVPR22(17000-17009)
IEEE DOI 2210
Laser radar, Computational modeling, Urban areas, Radar, Radar imaging, Laser modes, Cameras, Deep learning architectures and techniques BibRef

Doshi, K.[Keval], Yilmaz, Y.[Yasin],
Multi-Task Learning for Video Surveillance with Limited Data,
CLVision22(3888-3898)
IEEE DOI 2210
Training, Measurement, Transfer learning, Semantics, Training data, Multitasking, Video surveillance BibRef

Wallingford, M.[Matthew], Li, H.[Hao], Achille, A.[Alessandro], Ravichandran, A.[Avinash], Fowlkes, C.[Charless], Bhotika, R.[Rahul], Soatto, S.[Stefano],
Task Adaptive Parameter Sharing for Multi-Task Learning,
CVPR22(7551-7560)
IEEE DOI 2210
Training, Adaptation models, Costs, Multitasking, Task analysis, Transfer/low-shot/long-tail learning BibRef

Sun, T.[Tao], Segu, M.[Mattia], Postels, J.[Janis], Wang, Y.X.[Yu-Xuan], Van Gool, L.J.[Luc J.], Schiele, B.[Bernt], Tombari, F.[Federico], Yu, F.[Fisher],
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation,
CVPR22(21339-21350)
IEEE DOI 2210
Adaptation models, Uncertainty, Rain, Annotations, System performance, Data collection, Multitasking, Transfer/low-shot/long-tail learning BibRef

Wang, Z.Y.[Zhen-Yi], Shen, L.[Li], Duan, T.[Tiehang], Zhan, D.L.[Dong-Lin], Fang, L.[Le], Gao, M.C.[Ming-Chen],
Learning to Learn and Remember Super Long Multi-Domain Task Sequence,
CVPR22(7972-7982)
IEEE DOI 2210
Training, Benchmark testing, Task analysis, Transfer/low-shot/long-tail learning BibRef

Chen, L.[Lin], Chen, H.[Huaian], Wei, Z.X.[Zhi-Xiang], Jin, X.[Xin], Tan, X.[Xiao], Jin, Y.[Yi], Chen, E.[Enhong],
Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation,
CVPR22(7171-7180)
IEEE DOI 2210
Codes, Games, Predictive models, Feature extraction, Adversarial machine learning, Generators, Self- semi- meta- unsupervised learning BibRef

Ghiasi, G.[Golnaz], Zoph, B.[Barret], Cubuk, E.D.[Ekin D.], Le, Q.V.[Quoc V.], Lin, T.Y.[Tsung-Yi],
Multi-Task Self-Training for Learning General Representations,
ICCV21(8836-8845)
IEEE DOI 2203
Training, Geometry, Visualization, Image recognition, Computational modeling, grouping and shape BibRef

Lee, J.H.[Jae-Han], Lee, C.[Chul], Kim, C.S.[Chang-Su],
Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing,
ICCV21(5087-5096)
IEEE DOI 2203
Training, Codes, Heuristic algorithms, Predictive models, Prediction algorithms, 3D from a single image and shape-from-x BibRef

Kong, Y.J.[Ya-Jing], Liu, L.[Liu], Wang, J.[Jun], Tao, D.C.[Da-Cheng],
Adaptive Curriculum Learning,
ICCV21(5047-5056)
IEEE DOI 2203
Learning systems, Adaptation models, Analytical models, Nonuniform sampling, Classification algorithms, Task analysis, Representation learning BibRef

Wang, Y.F.[Yu-Feng], Tsai, Y.H.[Yi-Hsuan], Hung, W.C.[Wei-Chih], Ding, W.R.[Wen-Rui], Liu, S.[Shuo], Yang, M.H.[Ming-Hsuan],
Semi-supervised Multi-task Learning for Semantics and Depth,
WACV22(2663-2672)
IEEE DOI 2202
Training, Annotations, Semantics, Estimation, Benchmark testing, Predictive models, Multitasking, Semi- and Un- supervised Learning BibRef

Levi, H.[Hila], Ullman, S.[Shimon],
Multi-Task Learning By A Top-Down Control Network,
ICIP21(2553-2557)
IEEE DOI 2201
Image recognition, Machine vision, Modulation, Task analysis, Multi-task learning, Deep learning BibRef

Choi, H.[Hyomin], Bajic, I.V.[Ivan V.],
Latent-Space Scalability for Multi-Task Collaborative Intelligence,
ICIP21(3562-3566)
IEEE DOI 2201
Training, Image coding, Scalability, System performance, Object detection, Benchmark testing, Deep feature compression, video coding for machines BibRef

Kim, D.H.[Dong-Hyun], Lan, T.[Tian], Zou, C.H.[Chu-Hang], Xu, N.[Ning], Plummer, B.A.[Bryan A.], Sclaroff, S.[Stan], Eledath, J.[Jayan], Medioni, G.[Gérard],
MILA: Multi-Task Learning from Videos via Efficient Inter-Frame Attention,
DeepMTL21(2219-2229)
IEEE DOI 2112
Computational modeling, Benchmark testing, Feature extraction BibRef

Tan, Y.[Yang], Li, Y.[Yang], Huang, S.L.[Shao-Lun],
OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations,
CVPR21(15774-15783)
IEEE DOI 2111
Measurement, Training, Knowledge engineering, Uncertainty, Transfer learning, Neural networks, Estimation BibRef

Djolonga, J.[Josip], Yung, J.[Jessica], Tschannen, M.[Michael], Romijnders, R.[Rob], Beyer, L.[Lucas], Kolesnikov, A.[Alexander], Puigcerver, J.[Joan], Minderer, M.[Matthias], d'Amour, A.[Alexander], Moldovan, D.[Dan], Gelly, S.[Sylvain], Houlsby, N.[Neil], Zhai, X.H.[Xiao-Hua], Lucic, M.[Mario],
On Robustness and Transferability of Convolutional Neural Networks,
CVPR21(16453-16463)
IEEE DOI 2111
Training, Visualization, Systematics, Image resolution, Transfer learning, Pipelines, Robustness BibRef

Cai, J.[John], Cai, B.[Bill], Mei, S.S.[Shen Sheng],
DAMSL: Domain Agnostic Meta Score-based Learning,
LLID21(2591-2595)
IEEE DOI 2109
Computational modeling, Performance gain, Benchmark testing, Boosting BibRef

Khattar, A.[Apoorv], Hegde, S.[Srinidhi], Hebbalaguppe, R.[Ramya],
Cross-Domain Multi-task Learning for Object Detection and Saliency Estimation,
OmniCV21(3634-3643)
IEEE DOI 2109
Training, Neural networks, Estimation, Object detection BibRef

Wang, Q.F.[Qi-Fei], Ke, J.J.[Jun-Jie], Greaves, J.[Joshua], Chu, G.[Grace], Bender, G.[Gabriel], Sbaiz, L.[Luciano], Go, A.[Alec], Howard, A.[Andrew], Yang, M.H.[Ming-Hsuan], Gilbert, J.[Jeff], Milanfar, P.[Peyman], Yang, F.[Feng],
Multi-path Neural Networks for On-device Multi-domain Visual Classification,
WACV21(3018-3027)
IEEE DOI 2106
Training, Visualization, Adaptation models, Computational modeling, Scalability, Interference, Reinforcement learning BibRef

Li, Z.Z.[Zhi-Zhong], Luo, L.J.[Lin-Jie], Tulyakov, S.[Sergey], Dai, Q.[Qieyun], Hoiem, D.[Derek],
Task-Assisted Domain Adaptation with Anchor Tasks,
WACV21(2988-2997)
IEEE DOI 2106
Training, Image segmentation, Shape, Annotations, Semantics BibRef

Frecon, J.[Jordan], Salzo, S.[Saverio], Pontil, M.[Massimiliano],
Unveiling Groups of Related Tasks in Multi-Task Learning,
ICPR21(7134-7141)
IEEE DOI 2105
Benchmark testing, Approximation algorithms, Computational efficiency, Task analysis, Optimization, Standards BibRef

Yang, S.M.[Shih-Min], Yeh, M.C.[Mei-Chen],
Unsupervised Multi-Task Domain Adaptation,
ICPR21(1679-1685)
IEEE DOI 2105
Adaptation models, Image recognition, Target recognition, Annotations, Training data, Image representation BibRef

Spadotto, T.[Teo], Toldo, M.[Marco], Michieli, U.[Umberto], Zanuttigh, P.[Pietro],
Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training,
ICPR21(2845-2852)
IEEE DOI 2105
Adaptation models, Roads, Semantics, Neural networks, Reliability engineering, Robustness, Data models BibRef

Senhaji, A.[Ali], Raitoharju, J.[Jenni], Gabbouj, M.[Moncef], Iosifidis, A.[Alexandros],
Not all domains are equally complex: Adaptive Multi-Domain Learning,
ICPR21(8663-8670)
IEEE DOI 2105
Training, Deep learning, Adaptation models, Adaptive systems, Neural networks, Complexity theory BibRef

Takeda, M.[Mana], Benitez, G.[Gibran], Yanai, K.[Keiji],
Training of Multiple and Mixed Tasks with a Single Network Using Feature Modulation,
DLPR20(719-735).
Springer DOI 2103
BibRef

Zhu, R., Yan, L.,
Neighbour-based Domain Adaptation for Investigation of Transferable Ability of Previously Labeled Data for Land-cover Classification Of Aerial Images,
ISPRS20(B2:1329-1335).
DOI Link 2012
BibRef

Jain, H.[Himalaya], Gidaris, S.[Spyros], Komodakis, N.[Nikos], Pérez, P.[Patrick], Cord, M.[Matthieu],
Quest: Quantized Embedding Space for Transferring Knowledge,
ECCV20(XXI:173-189).
Springer DOI 2011
BibRef

Brüggemann, D.[David], Kanakis, M.[Menelaos], Obukhov, A.[Anton], Georgoulis, S.[Stamatios], Van Gool, L.J.[Luc J.],
Exploring Relational Context for Multi-Task Dense Prediction,
ICCV21(15849-15858)
IEEE DOI 2203
Computational modeling, Benchmark testing, Multitasking, Prediction algorithms, Task analysis, grouping and shape BibRef

Kanakis, M.[Menelaos], Bruggemann, D.[David], Saha, S.[Suman], Georgoulis, S.[Stamatios], Obukhov, A.[Anton], Van Gool, L.J.[Luc J.],
Reparameterizing Convolutions for Incremental Multi-Task Learning Without Task Interference,
ECCV20(XX:689-707).
Springer DOI 2011
BibRef

Huang, Z.Y.[Ze-Yi], Wang, H.H.[Hao-Han], Xing, E.P.[Eric P.], Huang, D.[Dong],
Self-challenging Improves Cross-domain Generalization,
ECCV20(II:124-140).
Springer DOI 2011
BibRef

Katzir, O.[Oren], Lischinski, D.[Dani], Cohen-Or, D.[Daniel],
Cross-domain Cascaded Deep Translation,
ECCV20(II:673-689).
Springer DOI 2011
BibRef

Sun, G.[Guolei], Probst, T.[Thomas], Paudel, D.P.[Danda Pani], Popovic, N.[Nikola], Kanakis, M.[Menelaos], Patel, J.[Jagruti], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
Task Switching Network for Multi-task Learning,
ICCV21(8271-8280)
IEEE DOI 2203
Switches, Benchmark testing, Multitasking, Decoding, Task analysis, Representation learning BibRef

Vandenhende, S.[Simon], Georgoulis, S.[Stamatios], Van Gool, L.J.[Luc J.],
Mti-net: Multi-scale Task Interaction Networks for Multi-task Learning,
ECCV20(IV:527-543).
Springer DOI 2011
BibRef

Tschannen, M.[Michael], Djolonga, J.[Josip], Ritter, M.[Marvin], Mahendran, A.[Aravindh], Houlsby, N.[Neil], Gelly, S.[Sylvain], Lucic, M.[Mario],
Self-Supervised Learning of Video-Induced Visual Invariances,
CVPR20(13803-13812)
IEEE DOI 2008
Videos, Task analysis, Visualization, Adaptation models, Data models, Benchmark testing, Image representation BibRef

Zhou, L., Cui, Z., Xu, C., Zhang, Z., Wang, C., Zhang, T., Yang, J.,
Pattern-Structure Diffusion for Multi-Task Learning,
CVPR20(4513-4522)
IEEE DOI 2008
Task analysis, Estimation, Image segmentation, Correlation, Semantics, Decoding, Sparse matrices BibRef

Lu, J., Goswami, V., Rohrbach, M., Parikh, D., Lee, S.,
12-in-1: Multi-Task Vision and Language Representation Learning,
CVPR20(10434-10443)
IEEE DOI 2008
Task analysis, Training, Visualization, Grounding, Image retrieval, Predictive models, Knowledge discovery BibRef

Wang, W., Tran, D., Feiszli, M.,
What Makes Training Multi-Modal Classification Networks Hard?,
CVPR20(12692-12702)
IEEE DOI 2008
Training, Task analysis, Kinetic theory, Optimization, Visualization, Benchmark testing BibRef

Jha, A., Kumar, A., Banerjee, B., Chaudhuri, S.,
AdaMT-Net: An Adaptive Weight Learning Based Multi-Task Learning Model For Scene Understanding,
EDLCV20(3027-3035)
IEEE DOI 2008
Task analysis, Decoding, Training, Adaptation models, Estimation, Semantics, Image segmentation BibRef

Choi, S., Hong, S., Lee, K., Lim, S.,
Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks,
CVPR20(3871-3880)
IEEE DOI 2008
Training, Robustness, Correlation, Task analysis, Noise measurement, Training data, Neural networks BibRef

Xia, Y., Liu, F., Yang, D., Cai, J., Yu, L., Zhu, Z., Xu, D., Yuille, A.L., Roth, H.,
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training,
WACV20(3635-3644)
IEEE DOI 2006
Training, Biomedical imaging, Task analysis, Uncertainty, Solid modeling BibRef

Al-Rawi, M., Valveny, E.,
Compact and Efficient Multitask Learning in Vision, Language and Speech,
CEFRL19(2933-2942)
IEEE DOI 2004
image classification, learning (artificial intelligence), speech recognition, image classification BibRef

Song, B.C., Kim, D.H., Lee, S.h.,
Metric-Based Regularization and Temporal Ensemble for Multi-Task Learning using Heterogeneous Unsupervised Tasks,
CEFRL19(2903-2912)
IEEE DOI 2004
learning (artificial intelligence), heterogeneous unsupervised tasks, target task, temporal task ensemble BibRef

Ni, F., Yao, Y.,
Multi-Task Learning via Scale Aware Feature Pyramid Networks and Effective Joint Head,
AutoNUE19(4265-4272)
IEEE DOI 2004
convolutional neural nets, feature extraction, image segmentation, learning (artificial intelligence), instance segmentation BibRef

Bragman, F.[Felix], Tanno, R.[Ryutaro], Ourselin, S.[Sebastien], Alexander, D.[Daniel], Cardoso, J.[Jorge],
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels,
ICCV19(1385-1394)
IEEE DOI 2004
convolutional neural nets, learning (artificial intelligence), probability, stochastic processes, stochastic filter groups, Cats BibRef

Ahn, C., Kim, E., Oh, S.,
Deep Elastic Networks With Model Selection for Multi-Task Learning,
ICCV19(6528-6537)
IEEE DOI 2004
feature selection, image classification, learning (artificial intelligence), neural net architecture, Computer architecture BibRef

Hassani, K., Haley, M.,
Unsupervised Multi-Task Feature Learning on Point Clouds,
ICCV19(8159-8170)
IEEE DOI 2004
feature extraction, graph theory, image classification, image reconstruction, image segmentation, pattern clustering, Convolution BibRef

Qin, C., Wang, L., Zhang, Y., Fu, Y.,
Generatively Inferential Co-Training for Unsupervised Domain Adaptation,
RLQ19(1055-1064)
IEEE DOI 2004
image classification, neural nets, unsupervised learning, Inferential BibRef

Maninis, K.K.[Kevis-Kokitsi], Radosavovic, I.[Ilija], Kokkinos, I.[Iasonas],
Attentive Single-Tasking of Multiple Tasks,
CVPR19(1851-1860).
IEEE DOI 2002
Network is trained on multiple tasks, but performs one task at a time. BibRef

Liu, S.K.[Shi-Kun], Johns, E.[Edward], Davison, A.J.[Andrew J.],
End-To-End Multi-Task Learning With Attention,
CVPR19(1871-1880).
IEEE DOI 2002
BibRef

Kawakami, R.[Rei], Yoshihashi, R.[Ryota], Fukuda, S.[Seiichiro], You, S.[Shaodi], Iida, M.[Makoto], Naemura, T.[Takeshi],
Cross-Connected Networks for Multi-Task Learning of Detection and Segmentation,
ICIP19(3636-3640)
IEEE DOI 1910
Multi-task Learning, Pedestrian Detection, Bird Detection, Semantic Segmentation BibRef

Javed, K.[Khurram], Shafait, F.[Faisal],
Revisiting Distillation and Incremental Classifier Learning,
ACCV18(VI:3-17).
Springer DOI 1906
Learn tasks incrementally BibRef

Mancini, M.[Massimiliano], Ricci, E.[Elisa], Caputo, B.[Barbara], Bulò, S.R.[Samuel Rota],
Adding New Tasks to a Single Network with Weight Transformations Using Binary Masks,
TASKCV18(II:180-189).
Springer DOI 1905
BibRef

Mallya, A.[Arun], Lazebnik, S.[Svetlana],
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning,
CVPR18(7765-7773)
IEEE DOI 1812
Task analysis, Training, Neural networks, Network architecture, Robustness, Training data BibRef

Kim, D.J., Choi, J., Oh, T.H., Yoon, Y., Kweon, I.S.,
Disjoint Multi-task Learning Between Heterogeneous Human-Centric Tasks,
WACV18(1699-1708)
IEEE DOI 1806
learning (artificial intelligence), optimisation, alternating directional optimization method, Visualization BibRef

Mallya, A.[Arun], Davis, D.[Dillon], Lazebnik, S.[Svetlana],
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights,
ECCV18(II: 72-88).
Springer DOI 1810
BibRef

Novotny, D.[David], Larlus, D.[Diane], Vedaldi, A.[Andrea],
AnchorNet: A Weakly Supervised Network to Learn Geometry-Sensitive Features for Semantic Matching,
CVPR17(2867-2876)
IEEE DOI 1711
Apply across tasks. Geometry, Proposals, Reliability, Semantics, Visualization. BibRef

Kuga, R.[Ryohei], Kanezaki, A.[Asako], Samejima, M.[Masaki], Sugano, Y.[Yusuke], Matsushita, Y.[Yasuyuki],
Multi-task Learning Using Multi-modal Encoder-Decoder Networks with Shared Skip Connections,
MSF17(403-411)
IEEE DOI 1802
Decoding, Feature extraction, Image segmentation, Neural networks, Semantics, Training BibRef

Ciliberto, C.[Carlo], Rosasco, L.[Lorenzo], Villa, S.[Silvia],
Learning multiple visual tasks while discovering their structure,
CVPR15(131-139)
IEEE DOI 1510
BibRef

Pentina, A.[Anastasia], Sharmanska, V.[Viktoriia], Lampert, C.H.[Christoph H.],
Curriculum learning of multiple tasks,
CVPR15(5492-5500)
IEEE DOI 1510
BibRef

Lapin, M.[Maksim], Schiele, B.[Bernt], Hein, M.[Matthias],
Scalable Multitask Representation Learning for Scene Classification,
CVPR14(1434-1441)
IEEE DOI 1409
BibRef

Tommasi, T.[Tatiana], Quadrianto, N.[Novi], Caputo, B.[Barbara], Lampert, C.H.[Christoph H.],
Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transfer,
ACCV12(I:1-15).
Springer DOI 1304
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Adversarial Networks for Transfer Learning, Domain Adaption .