D2
Computer Vision and Machine Learning

Yaoyao Liu (PhD Student)

Yaoyao Liu

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Location
E1 4 - 609
Phone
+49 681 9325 2109
Fax
+49 681 9325 2099

Personal Information

Publications

RMM: Reinforced Memory Management for Class-Incremental Learning
Y. Liu, B. Schiele and Q. Sun
Advances in Neural Information Processing Systems 34 Pre-Proceedings (NeurIPS 2021), 2021
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@inproceedings{Liu_Neurips2021, TITLE = {{RMM}: {R}einforced Memory Management for Class-Incremental Learning}, AUTHOR = {Liu, Yaoyao and Schiele, Bernt and Sun, Qianru}, LANGUAGE = {eng}, PUBLISHER = {Curran Associates, Inc.}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021)}, EDITOR = {Ranzato, M. and Beygelzimer, A. and Liang, P. S. and Vaughan, J. W. and Dauphin, Y.}, ADDRESS = {Virtual}, }
Adaptive Aggregation Networks for Class-Incremental Learning
Y. Liu, B. Schiele and Q. Sun
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), 2021
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@inproceedings{Liu_arXiv2010.05063, TITLE = {Adaptive Aggregation Networks for Class-Incremental Learning}, AUTHOR = {Liu, Yaoyao and Schiele, Bernt and Sun, Qianru}, LANGUAGE = {eng}, ISBN = {978-1-6654-4509-2}, DOI = {10.1109/CVPR46437.2021.00257}, PUBLISHER = {IEEE}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)}, PAGES = {2544--2553}, ADDRESS = {Nashville, TN, USA (Virtual)}, }
Generating Face Images With Attributes for Free
Y. Liu, Q. Sun, X. He, A.-A. Liu, Y. Su and T.-S. Chua
IEEE Transactions on Neural Networks and Learning Systems, Volume 32, Number 6, 2021
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@article{Liu2021, TITLE = {Generating Face Images With Attributes for Free}, AUTHOR = {Liu, Yaoyao and Sun, Qianru and He, Xiangnan and Liu, An-An and Su, Yuting and Chua, Tat-Seng}, LANGUAGE = {eng}, ISSN = {2162-237X}, DOI = {10.1109/TNNLS.2020.3007790}, PUBLISHER = {IEEE}, ADDRESS = {Piscataway, NJ}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, JOURNAL = {IEEE Transactions on Neural Networks and Learning Systems}, VOLUME = {32}, NUMBER = {6}, PAGES = {2733--2743}, }
Learning to Teach and Learn for Semi-supervised Few-shot Image Classification
X. Li, J. Huang, Y. Liu, Q. Zhou, S. Zheng, B. Schiele and Q. Sun
Computer Vision and Image Understanding, Volume 212, 2021
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@article{Li2021, TITLE = {Learning to Teach and Learn for Semi-supervised Few-shot Image Classification}, AUTHOR = {Li, Xinzhe and Huang, Jianqiang and Liu, Yaoyao and Zhou, Qin and Zheng, Shibao and Schiele, Bernt and Sun, Qianru}, LANGUAGE = {eng}, ISSN = {1077-3142}, DOI = {10.1016/j.cviu.2021.103270}, PUBLISHER = {Elsevier}, ADDRESS = {Amsterdam}, YEAR = {2021}, MARGINALMARK = {$\bullet$}, DATE = {2021}, JOURNAL = {Computer Vision and Image Understanding}, VOLUME = {212}, EID = {103270}, }
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning
Y. Liu, B. Schiele and Q. Sun
Computer Vision -- ECCV 2020, 2020
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@inproceedings{Liu_ECCV2020, TITLE = {An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning}, AUTHOR = {Liu, Yaoyao and Schiele, Bernt and Sun, Qianru}, LANGUAGE = {eng}, ISBN = {978-3-030-58516-7}, DOI = {10.1007/978-3-030-58517-4_24}, PUBLISHER = {Springer}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, DATE = {2020}, BOOKTITLE = {Computer Vision -- ECCV 2020}, EDITOR = {Vedaldi, Andrea and Bischof, Horst and Brox, Thomas and Frahm, Jan-Michael}, PAGES = {404--421}, SERIES = {Lecture Notes in Computer Science}, VOLUME = {12361}, ADDRESS = {Glasgow, UK}, }
Mnemonics Training: Multi-Class Incremental Learning Without Forgetting
Y. Liu, Y. Su, A.-A. Liu, B. Schiele and Q. Sun
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), 2020
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@inproceedings{Liu_2020_CVPR, TITLE = {Mnemonics Training: {M}ulti-Class Incremental Learning Without Forgetting}, AUTHOR = {Liu, Yaoyao and Su, Yuting and Liu, An-An and Schiele, Bernt and Sun, Qianru}, LANGUAGE = {eng}, ISBN = {978-1-7281-7168-5}, DOI = {10.1109/CVPR42600.2020.01226}, PUBLISHER = {IEEE}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)}, PAGES = {12242--12251}, ADDRESS = {Seattle, WA, USA (Virtual)}, }
Meta-Transfer Learning through Hard Tasks
Q. Sun, Y. Liu, Z. Chen, T.-S. Chua and B. Schiele
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
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@article{Sun__TPAMI2020, TITLE = {Meta-Transfer Learning through Hard Tasks}, AUTHOR = {Sun, Qianru and Liu, Yaoyao and Chen, Zhaozheng and Chua, Tat-Seng and Schiele, Bernt}, LANGUAGE = {eng}, ISSN = {0162-8828}, DOI = {10.1109/TPAMI.2020.3018506}, PUBLISHER = {IEEE}, ADDRESS = {Piscataway, NJ}, YEAR = {2020}, MARGINALMARK = {$\bullet$}, JOURNAL = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, }
Learning to Self-Train for Semi-Supervised Few-Shot Classification
X. Li, Q. Sun, Y. Liu, Q. Zhou, S. Zheng, T.-S. Chua and B. Schiele
Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019
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@inproceedings{Li_NeurIPs2019, TITLE = {Learning to Self-Train for Semi-Supervised Few-Shot Classification}, AUTHOR = {Li, Xinzhe and Sun, Qianru and Liu, Yaoyao and Zhou, Qin and Zheng, Shibao and Chua, Tat-Seng and Schiele, Bernt}, LANGUAGE = {eng}, URL = {http://papers.nips.cc/paper/9216-learning-to-self-train-for-semi-supervised-few-shot-classification.pdf}, PUBLISHER = {Curran Associates, Inc.}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {Advances in Neural Information Processing Systems 32 (NeurIPS 2019)}, EDITOR = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and d'Alch{\'e}-Buc, F. and Fox, E. and Garnett, R.}, PAGES = {10276--10286}, ADDRESS = {Vancouver, Canada}, }
Meta-Transfer Learning for Few-Shot Learning
Q. Sun, Y. Liu, T.-S. Chua and B. Schiele
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), 2019
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@inproceedings{Sun_CVPR2019, TITLE = {Meta-Transfer Learning for Few-Shot Learning}, AUTHOR = {Sun, Qianru and Liu, Yaoyao and Chua, Tat-Seng and Schiele, Bernt}, LANGUAGE = {eng}, ISBN = {978-1-7281-3293-8}, DOI = {10.1109/CVPR.2019.00049}, PUBLISHER = {IEEE}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019)}, PAGES = {403--412}, ADDRESS = {Long Beach, CA, USA}, }
LCC: Learning to Customize and Combine Neural Networks for Few-Shot Learning
Y. Liu, Q. Sun, A.-A. Liu, Y. Su, B. Schiele and T.-S. Chua
Technical Report, 2019
(arXiv: 1904.08479)
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@online{Liu_arXiv1904.08479, TITLE = {{LCC}: {L}earning to Customize and Combine Neural Networks for Few-Shot Learning}, AUTHOR = {Liu, Yaoyao and Sun, Qianru and Liu, An-An and Su, Yuting and Schiele, Bernt and Chua, Tat-Seng}, LANGUAGE = {eng}, URL = {http://arxiv.org/abs/1904.08479}, EPRINT = {1904.08479}, EPRINTTYPE = {arXiv}, YEAR = {2019}, MARGINALMARK = {$\bullet$}, ABSTRACT = {Meta-learning has been shown to be an effective strategy for few-shot learning. The key idea is to leverage a large number of similar few-shot tasks in order to meta-learn how to best initiate a (single) base-learner for novel few-shot tasks. While meta-learning how to initialize a base-learner has shown promising results, it is well known that hyperparameter settings such as the learning rate and the weighting of the regularization term are important to achieve best performance. We thus propose to also meta-learn these hyperparameters and in fact learn a time- and layer-varying scheme for learning a base-learner on novel tasks. Additionally, we propose to learn not only a single base-learner but an ensemble of several base-learners to obtain more robust results. While ensembles of learners have shown to improve performance in various settings, this is challenging for few-shot learning tasks due to the limited number of training samples. Therefore, our approach also aims to meta-learn how to effectively combine several base-learners. We conduct extensive experiments and report top performance for five-class few-shot recognition tasks on two challenging benchmarks: miniImageNet and Fewshot-CIFAR100 (FC100).}, }