Yongqin Xian
Collaborating Research Scientist
yongqin.xian at vision.ee.ethz.ch

Profile

I am currently a research scientist at Google Zurich. Prior to that, I was a post-doctoral researcher with Luc Van Gool in the Computer Vision Lab at ETH Zurich. I completed my PhD summa cum laude at the Max Planck Institute Informatics under the supervision of Bernt Schiele and Zeynep Akata. My research focuses on zero-shot learning, few-shot learning and visual-language models.

Research

Yongqin Xian is mainly interested in solving computer vision tasks with limited supervision. For instance, zero-shot learning that learns to recognize unseen classes without any labeled data and few-shot learning that learns to recognize novel classes with only few labeled data. Besides, he is also interested in related topics on semi-supervised, unsupervised learning and self-supervised learning.

Publications

  • "Attribute Prototype Network for Any-Shot Learning", Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata, International Journal of Computer Vision, IJCV 2022
  • "VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning", Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2022
  • "Learning Graph Embeddings for Open World Compositional Zero-Shot Learning", Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, Zeynep Akata, IEEE Transactions on Pattern Analysis and Machine Intelligence, TPAMI 2022
  • "Prototype-based Incremental Few-Shot Segmentation", Fabio Cermelli, Massimiliano Mancini, Yongqin Xian, Zeynep Akata, Barbara Caputo, British Machine Vision Conference, BMVC 2021
  • "Learning Graph Embeddings for Compositional Zero-shot Learning", Mohammad Ferjad Naeem, Yongqin Xian, Federico Tombari, Zeynep Akata, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021
  • "Distilling Audio-Visual Knowledge by Compositional Contrastive Learning", Yanbei Chen, Yongqin Xian, A. Sophia Koepke, Zeynep Akata, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021
  • "Open World Compositional Zero-Shot Learning", Massimiliano Mancini, Mohammad Ferjad Naeem, Yongqin Xian, Zeynep Akata, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021
  • "Attribute Prototype Network for Zero-Shot Learning", Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata, Neural Information Processing Systems (NeurIPS), 2020
  • "Zero-shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly", Yongqin Xian, Christoph H. Lampert, Bernt Schiele and Zeynep Akata, IEEE TPAMI link: https://arxiv.org/pdf/1703.04394.pdf
  • "SPNet: Semantic Projection Network for Zero- and Few-Label Semantic Segmentation", Yongqin Xian*, Subhabrata Choudhury*, Yang He, Bernt Schiele, and Zeynep Akata, (*indicate equal contribution), IEEE CVPR 2019, link: https://pdfs.semanticscholar.org/ea8d/6c2de162e0f9ad89af7b950333cb29e94622.pdf
  • "f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning", Yongqin Xian, Saurabh Sharma, Bernt Schiele, and Zeynep Akata, IEEE CVPR 2019, link: https://arxiv.org/pdf/1903.10132.pdf
  • "Feature Generating Networks for Zero-Shot Learning", Yongqin Xian, Tobias Lorenz, Bernt Schiele, and Zeynep Akata, IEEE CVPR 2018, link: https://arxiv.org/pdf/1712.00981v2.pdf
  • "Zero-shot learning - The Good, the Bad and the Ugly", Yongqin Xian, Bernt Schiele, and Zeynep Akata. IEEE CVPR 2017, link: https://arxiv.org/pdf/1707.00600.pdf
  • "Latent Embeddings for Zero-shot Classification", Yongqin Xian , Zeynep Akata , Gaurav Sharma,Quynh Nguyen, Matthias Hein and Bernt Schiele, IEEE CVPR 2016 link: https://arxiv.org/pdf/1603.08895.pdf