SelectedWorks - Huan Xiong

Selected Works of Huan Xiong

Assistant Professor, Department of Machine Learning, MBZUAI

Dr. Huan Xiong is an Assistant Professor in the Machine Learning Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).

Biography

Dr. Huan Xiong has been a Research Scientist at the Inception Institute of Artificial Intelligence (IIAI), United Arab Emirates, and was a postdoctoral researcher at the Institute for Advanced Mathematical Research, University of Strasbourg, France, from 2016 to 2018. He received his B.S. and M.S. degrees from the School of Mathematical Sciences, Peking University, China, in 2010 and 2013, respectively, and the Ph.D. degree from the Institute of Mathematics, University of Zurich, Switzerland, in 2016. He hosted two research projects funded by the Swiss National Science Foundation (SNSF) and the French National Centre for Scientific Research (CNRS) respectively. His research interests include machine learning and discrete mathematics.

Works
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About Huan Xiong

Dr. Huan Xiong is an Assistant Professor in the Machine Learning Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).

Biography

Dr. Huan Xiong has been a Research Scientist at the Inception Institute of Artificial Intelligence (IIAI), United Arab Emirates, and was a postdoctoral researcher at the Institute for Advanced Mathematical Research, University of Strasbourg, France, from 2016 to 2018. He received his B.S. and M.S. degrees from the School of Mathematical Sciences, Peking University, China, in 2010 and 2013, respectively, and the Ph.D. degree from the Institute of Mathematics, University of Zurich, Switzerland, in 2016. He hosted two research projects funded by the Swiss National Science Foundation (SNSF) and the French National Centre for Scientific Research (CNRS) respectively. His research interests include machine learning and discrete mathematics.

Present	Assistant Professor, Mohamed bin Zayed University of Artificial Intelligence ‐ Department of Machine Learning
Institution Organization Position Type Position Title Time Period to Options Current Position Affiliate my profile with this Institution

Computer Sciences, Artificial Intelligence and Robotics, Statistics and Probability, and Data Science

Machine learning, deep learning, and and discrete mathematics

http://orcid.org/0000-0001-5401-7834

Memory-Aided Contrastive Consensus Learning for Co-salient Object Detection (Peng Zheng, Jie Qin, Shuo Wang, et al.) Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 (2023)

Co-salient object detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. Most of the latest works employ the attention mechanism for finding common objects. To achieve accurate CoSOD results ...

Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation (Guo-Sen Xie, Jie Liu, Huan Xiong, et al.) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2021)

Few-shot semantic segmentation (FSS) aims to segment unseen class objects given very few densely-annotated support images from the same class. Existing FSS methods find the query object by using support prototypes or by directly relying ...

Learning to Learn with Variational Information Bottleneck for Domain Generalization (Yingjun Du, Jun Xu, Huan Xiong, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2020)

Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift. In this paper, we address both problems. We introduce a probabilistic meta-learning model for domain generalization, in ...

On the Number of Linear Regions of Convolutional Neural Networks (Huan Xiong, Lei Huang, Mengyang Yu, et al.) arXiv: Learning (2020)

One fundamental problem in deep learning is understanding the outstanding performance of deep Neural Networks (NNs) in practice. One explanation for the superiority of NNs is that they can realize a large class of complicated ...

Learning to Learn with Variational Information Bottleneck for Domain Generalization (Yingjun Du, Jun Xu, Huan Xiong, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2020)

© 2020, Springer Nature Switzerland AG. Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift. In this paper, we address both problems. We introduce a probabilistic ...

On the Number of Linear Regions of Convolutional Neural Networks (Huan Xiong, Lei Huang, Mengyang Yu, et al.) 37th International Conference on Machine Learning, ICML 2020 (2020)

One fundamental problem in deep learning is understanding the outstanding performance of deep Neural Networks (NNs) in practice. One explanation for the superiority of NNs is that they can realize a large class of complicated ...

Journal Articles (2)

Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge (Jun Ma, Yao Zhang, Song Gu, et al.) Medical Image Analysis (2022)

Automatic segmentation of abdominal organs in CT scans plays an important role in clinical practice. However, most existing benchmarks and datasets only focus on segmentation accuracy, while the model efficiency and its accuracy on the ...

A Generalized Method for Binary Optimization: Convergence Analysis and Applications (Huan Xiong, Mengyang Yu, Li Liu, et al.) IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)

Binary optimization problems (BOPs) arise naturally in many fields, such as information retrieval, computer vision, and machine learning. Most existing binary optimization methods either use continuous relaxation which can cause large quantization errors, or incorporate ...

Balanced Self-Paced Learning for AUC Maximization (Bin Gu, Chenkang Zhang, Huan Xiong, et al.) arXiv (2022)

Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. Self-paced learning is an effective method for handling noisy data. ...

Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation (Jie Liu, Yanqi Bao, Guo-Sen Xie, et al.) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2022)

The key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among sup-port and query features and/or their prototypes, under the episodic training scenario. Most existing FSS methods im-plement such support/query ...

Implicit Motion Handling for Video Camouflaged Object Detection (Xuelian Cheng, Huan Xiong, Deng-Ping Fan, et al.) arXiv (2022)

We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames. An essential property of camouflaged objects is that ...

Learning to Learn Variational Semantic Memory (Xiantong Zhen, Yingjun Du, Huan Xiong, et al.) arXiv (2021)

In this paper, we introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The variational semantic memory accrues and stores semantic information for the probabilistic inference of class prototypes in a ...