Yongqin Xian

PhD Researcher

Yongqin Xian is currently a PhD student at  Max Planck Institute Informatics, advised by Zeynep Akata and Bernt Schiele. He received his master degree in computer science with honors at Saarland University, Germany and bachelor degree in computer science at Beijing Institute of Technology, China. He was a research intern at Facebook AI with Lorenzo Torresani.


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

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

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

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

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

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
 

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.