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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
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Short description of portfolio item number 1
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Short description of portfolio item number 2 
Published in CVPR, 2019 (Oral)
Authors: Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang
In this work, we build a graph learning framework to employ context information for person search.
Published in ICCV, 2019
Authors: Jian Zhang, Chenglong Zhao, Bingbing Ni, Minghao Xu, Xiaokang Yang
In this work, we propose a variational Bayesian framework to approximate bias-eliminated class specific sample distributions for few-shot learning.
Published in TPAMI, 2019
Authors: Yichao Yan, Ning Zhuang, Bingbing Ni, Jian Zhang, Minghao Xu, Qiang Zhang, Zhang Zheng, Shuo Cheng, Qi Tian, Xiaokang Yang, Wenjun Zhang
In this work, we collect a new video dataset from YouTube.com, called Sports Video Narrative (SVN), for fine-grained sports auto-narrative. Also, we propose a novel framework to encode both intra- and inter- team interactions.
Published in AAAI, 2020 (Oral)
Authors: Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, Wenjun Zhang
In this work, we propose to promote adversarial domain adaptation with both pixel-level and feature-level domain mixup.
Published in CVPR, 2020 (Oral)
Authors: Minghao Xu, Hang Wang, Bingbing Ni, Qi Tian, Wenjun Zhang
In this work, we propose a Graph-induced Prototype Alignment (GPA) framework to align source and target domain for cross-domain detection tasks.
Published in ECCV, 2020
Authors: Hang Wang*, Minghao Xu*, Bingbing Ni, Wenjun Zhang (* equal contribution)
In this work, we propose a Learning to Combine framework, in which the knowledges acquired from multiple source domains are aggregated and applied to the target domain.
Published in ICML, 2021
Authors: Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, Jian Tang
Published in ICCV, 2021
Authors: Minghao Xu, Hang Wang, Bingbing Ni, Riheng Zhu, Zhenbang Sun, Changhu Wang
Published in ICCV, 2021
Authors: Qiqi Gu, Qianyu Zhou, Minghao Xu, Zhengyang Feng, Guangliang Cheng, Xuequan Lu, Jianping Shi, Lizhuang Ma
Published in NeurIPS, 2021
Authors: Minghao Xu, Meng Qu, Bingbing Ni, Jian Tang
Published in TPAMI, 2022
Authors: Minghao Xu, Hang Wang, Bingbing Ni
In this work, we propose a Conditional Random Field (CRF) method and another Markov Random Field (MRF) method to solve the Multi-Source Domain Adaptation problem.
Published in arXiv, 2022
Authors: Minghao Xu*, Yuanfan Guo*, Xuanyu Zhu, Jiawen Li, Zhenbang Sun, Jian Tang, Yi Xu, Bingbing Ni (* equal contribution)
Published in CVPR, 2022
Authors: Yuanfan Guo*, Minghao Xu*, Jiawen Li, Bingbing Ni, Xuanyu Zhu, Zhenbang Sun, Yi Xu (* equal contribution)
Published in NeurIPS, 2022
Authors: Minghao Xu*, Zuobai Zhang*, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang (* equal contribution)
Published in ICLR MLDD Workshop, 2023
Authors: Minghao Xu, Yuanfan Guo, Yi Xu, Jian Tang, Xinlei Chen, Yuandong Tian
Published in ICLR, 2023
Authors: Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang
Published in ICML, 2023 (Oral)
Authors: Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang
Published in NeurIPS, 2023 (Spotlight)
Authors: Zuobai Zhang*, Minghao Xu*, Aurelie Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang (* equal contribution)
Published in ACL, 2024
Authors: Le Zhuo*, Zewen Chi*, Minghao Xu*, Heyan Huang, Jianan Zhao, Heqi Zheng, Conghui He, Xian-Ling Mao, Wentao Zhang (* equal contribution)
Published in AAAI, 2020
This is a remote talk on the AAAI 2020 conference. In this work, we propose to promote adversarial domain adaptation with both pixel-level and feature-level domain mixup.
Published in CVPR, 2020
This is a remote talk on the CVPR 2020 conference. In this work, we propose a Graph-induced Prototype Alignment (GPA) framework to align source and target domain for cross-domain detection tasks.
Published in ECCV, 2020
This is a remote talk on the ECCV 2020 conference. In this work, we propose a Learning to Combine framework, in which the knowledges acquired from multiple source domains are aggregated and applied to the target domain.
Published in ICML, 2021
This is a remote talk on the ICML 2021 conference. In this work, we propose a principled framework to learn both the local-instance and global-semantic structure of graph representations in a self-supervised way.
Published in University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Published in University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.