Yongqin Xian (PhD Student)

MSc Yongqin Xian

Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
E1 4 - Room 628
+49 681 9325 2028
+49 681 9325 2099
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Personal Information

Research Interests

  • Computer Vision
  • Machine Learning


  • 2016-present, Ph.D. candidate in Computer Science, Max Planck Institute for Informatics, Germany
  • 2013-2015, M.Sc.(Honors Degree) in Computer Science, Saarland University, Germany
  • 2009-2013, B.E. in Software Engineering, Beijing Institute of Technology, China 


  • Teaching Assistant, Machine Learning, Winter Semester 2015/2016 (taught by Matthias Hein)


Reviewing Activities

  • Reviewer in Journals: TPAMI 2017-, TIP 2018-, TOMM 2017-
  • Reviewer in Conferences: CVPR 2018, ACCV 2018


Feature Generating Networks for Zero-Shot Learning
Y. Xian, T. Lorenz, B. Schiele and Z. Akata
31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018
(Accepted/in press)
Zero-shot learning - The Good, the Bad and the Ugly
Y. Xian, B. Schiele and Z. Akata
30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), 2017
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
Y. Xian, C. H. Lampert, B. Schiele and Z. Akata
Technical Report, 2017
(arXiv: 1707.00600)
Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.
Latent Embeddings for Zero-shot Classification
Y. Xian, Z. Akata, G. Sharma, Q. Nguyen, M. Hein and B. Schiele
29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 2016