Jian Hu

I am currently a PhD student within the Computer Vision Group in the School of Electronic Engineering and Computer Science at Queen Mary, University of London, supervised by Prof. Shaogang Gong.

Before joining the Computer Vision Group in QMUL, I was in Shanghai Jiao Tong University , supervised by Prof. Hongya Tuo and working closely with Prof. Junchi Yan.

Currently my area of expertise is in deep learning and computer vision, especially in Transfer learning, Semi-supervised learning, Incremental learning, Lifelong Learning and their related applications.

Email / Google Scholar / Website / Github

Research

I'm interested in computer vision, machine learning, and image processing. I am currently primarily focused on how to achieve cross-domain knowledge transfer in in-the-wild scenarios.

News

2023-12: GenSAM is accepted to AAAI 2024.

2023-05: UHPKD is accepted to SIGIR 2023.

	Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects Jian Hu, Jiayi Lin, Weitong Cai, Shaogang gong AAAI, 2024 arXiv / website / code / bibtex Eliminate the need for manual prompts for SAM in various challenging segmentation tasks.
	Uncertainty-based Heterogeneous Privileged Knowledge Distillation for Recommendation System Ang Li, Jian Hu, Ke Ding, Xiaolu Zhang, Jun Zhou, Yong He SIGIR, 2023 arXiv / bibtex Proposing a novel algorithm to address heterogeneous knowledge distillation-based transfer learning in industrial recommendation systems.
	Global-Aware Model-Free Self-distillation for Recommendation System Ang Li, Jian Hu, Lu Wei, Ke Ding, Xiaolu Zhang, Jun Zhou, Yong He DASFAA, 2023 paper / bibtex Introducing a novel algorithm called Global-aware Model-free Self-Distillation to address label noise in training data in Alipay advertising system.
	Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling Jian Hu, Haowen Zhong, Fei Yang, Shaogang gong, Guile Wu, Junchi Yan ECCV, 2022 arXiv / code / bibtex Delving into the transferability estimation problem in domain adaptation and propose a non-intrusive Unbiased Transferability Estimation Plug-in (UTEP) by modeling the uncertainty of a discriminator in adversarial-based DA methods to optimize unbiased transfer.
	Domain adaptive YOLO for one-stage cross-domain detection Shizhao Zhang, Hongya Tuo, Zhongliang Jing, Jian Hu ACML, 2021 arXiv / bibtex Improving cross-domain performance for one-stage detectors, image level features alignment is used to strictly match for local features, and loosely match for global features.
	Discriminative Partial Domain Adversarial Network Jian Hu, Hongya Tuo, Chao Wang, Lingfeng Qiao, Haowen Zhong, Junchi Yan, Zhongliang Jing, Henry Leung ECCV, 2020 arXiv / bibtex Addressing partial domain adaptation problem with discriminative partial domain adversarial network with theoretical analysis.
	Unsupervised satellite image classification based on partial transfer learning Jian Hu, Hongya Tuo, Chao Wang, Haowen Zhong, Pan Han, Lingfeng Qiao, Zhongliang Jing Aerospace Systems, 2019 arXiv / bibtex Focusing on how to achieve high accuracy on unsupervised satellite image classification.
	Multi-Weight Partial Domain Adaptation Jian Hu, Hongya Tuo, Chao Wang, Lingfeng Qiao, Haowen Zhong, Zhongliang Jing BMVC, 2019 (spotlight) arXiv / bibtex Focusing on how to transfer knowledge from massive labelled dataset to unlabelled miniature one.

Service

	Reviewer for CVPR, ICCV, ECCV, BMVC, ICML, ACMMM, PKDD
	Student Demonstrator, Deep Learning and Computer Vision 2022 Student Demonstrator Data Mining 2023 Student Demonstrator, Deep Learning and Computer Vision 2021

Special thank you to the source code.