Yongqin Xian (PhD Student)

MSc Yongqin Xian
- Address
- Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken - Location
- E1 4 - Room 618
- Phone
- +49 681 9325 2118
- Fax
- +49 681 9325 2099
- Get email via email
Personal Information
Research Interests
- Computer Vision
- Machine Learning
Education
- 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 Computer Science, Beijing Institute of Technology, China
Teaching
- Teaching Assistant, Machine Learning, Winter Semester 2015/2016 (taught by Matthias Hein)
Project
- Latent Embedding for Zero-shot Classification
- Zero-Shot Learning - The Good, the Bad and the Ugly
- Feature Generating Networks for Zero-shot Learning
Reviewing Activities
- Reviewer in Journals: TPAMI 2017-, TIP 2018-, TOMM 2017-
- Reviewer in Conferences: ICML 2019, CVPR 2019, ICLR 2019, CVPR 2018, NIPS 2018, ACCV 2018
Publications
2018
Zero-shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly
Y. Xian, C. H. Lampert, B. Schiele and Z. Akata
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018
(Accepted/in press) Y. Xian, C. H. Lampert, B. Schiele and Z. Akata
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018
Abstract
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.
2017
2016