Yaoyao Liu (PhD Student)

Personal Information

Publications

2023

  1. Conference paper
    D2
    “Class-Incremental Exemplar Compression for Class-Incremental Learning,” in 36th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), Vancouver, Canada, 2023.
  2. Conference paper
    D2
    “Continual Detection Transformer for Incremental Object Detection,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), Vancouver, Canada, 2023.
  3. Conference paper
    D2
    “Online Hyperparameter Optimization for Class-Incremental Learning,” in Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, DC, USA, 2023.
  4. Thesis
    D2IMPR-CS
    “Learning from Imperfect Data Incremental Learning and Few-shot Learning,” Universität des Saarlandes, Saarbrücken, 2023.

2022

  1. Article
    D2
    “Meta-Transfer Learning through Hard Tasks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 3, 2022.

2021

  1. Conference paper
    D2
    “RMM: Reinforced Memory Management for Class-Incremental Learning,” in Advances in Neural Information Processing Systems 34 (NeurIPS 2021), Virtual, 2021.
  2. Article
    D2
    “Learning to Teach and Learn for Semi-supervised Few-shot Image Classification,” Computer Vision and Image Understanding, vol. 212, 2021.
  3. Conference paper
    D2
    “Adaptive Aggregation Networks for Class-Incremental Learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021), Nashville, TN, USA (Virtual), 2021.
  4. Article
    D2
    “Generating Face Images With Attributes for Free,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 6, 2021.

2020

  1. Conference paper
    D2
    “An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning,” in Computer Vision -- ECCV 2020, Glasgow, UK, 2020.
  2. Conference paper
    D2
    “Mnemonics Training: Multi-Class Incremental Learning Without Forgetting,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, USA (Virtual), 2020.

2019

  1. Conference paper
    D2
    “Learning to Self-Train for Semi-Supervised Few-Shot Classification,” in Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vancouver, Canada, 2019.
  2. Conference paper
    D2
    “Meta-Transfer Learning for Few-Shot Learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, USA, 2019.
  3. Paper
    D2
    “LCC: Learning to Customize and Combine Neural Networks for Few-Shot Learning,” 2019. [Online]. Available: http://arxiv.org/abs/1904.08479.