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

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, Volume 44, Number 3, 2022
Export
BibTeX
@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 = {2022}, MARGINALMARK = {$\bullet$}, DATE = {2022}, JOURNAL = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, VOLUME = {44}, NUMBER = {3}, PAGES = {1443--1456}, }
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
Export
BibTeX
@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
Export
BibTeX
@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
Export
BibTeX
@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
Export
BibTeX
@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
Export
BibTeX
@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}, 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
Export
BibTeX
@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}, BOOKTITLE = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)}, PAGES = {12242--12251}, ADDRESS = {Seattle, WA, USA (Virtual)}, }
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
Export
BibTeX
@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}, 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
Export
BibTeX
@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}, 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)
Export
BibTeX
@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}, ABSTRACT = {Meta-learning has been shown to be an effective strategy for few-shot<br>learning. The key idea is to leverage a large number of similar few-shot tasks<br>in order to meta-learn how to best initiate a (single) base-learner for novel<br>few-shot tasks. While meta-learning how to initialize a base-learner has shown<br>promising results, it is well known that hyperparameter settings such as the<br>learning rate and the weighting of the regularization term are important to<br>achieve best performance. We thus propose to also meta-learn these<br>hyperparameters and in fact learn a time- and layer-varying scheme for learning<br>a base-learner on novel tasks. Additionally, we propose to learn not only a<br>single base-learner but an ensemble of several base-learners to obtain more<br>robust results. While ensembles of learners have shown to improve performance<br>in various settings, this is challenging for few-shot learning tasks due to the<br>limited number of training samples. Therefore, our approach also aims to<br>meta-learn how to effectively combine several base-learners. We conduct<br>extensive experiments and report top performance for five-class few-shot<br>recognition tasks on two challenging benchmarks: miniImageNet and<br>Fewshot-CIFAR100 (FC100).<br>}, }