Jian Hu

I am currently a final year 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

profile photo

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

  • 2024-11: Be awarded as a top reviewer in NeurIPS 2024.
  • 2024-09: ProMaC is accepted to NeurIPS 2024.
  • 2024-06: Start a research internship in Spotify.
  • 2024-04: Give a talk in BMVA workshop: Trustworthy Multimodal Learning with Foundation Models: Bridging the Gap between AI Research and Real World Applications.
  • 2023-12: GenSAM is accepted to AAAI 2024.
  • 2023-05: UHPKD is accepted to SIGIR 2023.

    • Publications

    safs_small Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation
    Jian Hu, Jiayi Lin, Junchi Yan, Shaogang Gong
    NeurIPS, 2024
    arXiv / website / code / bibtex
    Using hallucinations as prior knowledge to help create specific prompts for segmenting tasks, reducing the need for manual prompts.
    safs_small 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.
    safs_small 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.
    safs_small 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.
    safs_small 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.
    safs_small 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.
    safs_small 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.
    safs_small 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.
    safs_small 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, TPAMI, IJCV, ICML, ICLR, NeurlPS (top reviewer'24), AAAI, ACMMM (outstanding reviewer'24), PKDD, AISTATS, ToMM
    cs188 Student Demonstrator, Deep Learning and Computer Vision 2021-24
    Student Demonstrator, Data Mining 2023

    Special thank you to the source code.