Hi there Welcome to my Homepage!

I am an undergraduate (2022-2026) at Xidian University, focusing on Vision-Language-Action. I work at MVIG@SJTU with Prof. Lixin Yang and Prof. Cewu Lu.

News

  • Unlesh the potential of Autoregressive policy: Dense Policy is accepted in ICCV 2025 🔥
  • MBA is accepted in IEEE RA-L 2025 🔥
  • Our work Advdisplay was accepted at AAAI 2025 🔥
  • In charge of Microsoft Club. Feel free to reach out if you’d like to join.

Experience

Shanghai Jiao Tong University
July 2024 - Now
Research intern at MVIG Lab
Xidian University
September 2022 - July 2026
B.Eng at AI College, RA at OMEGA Lab

Publications

RIaa
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification
Yue Su, Hao Li†, Maoguo Gong
A generative physical adversarial attack on VI-ReID models perturbs modality-invariant features.
ArXiv Preprint   [arxiv]


Raa
AdvDisplay: Adversarial Display Assembled by Thermoelectric Cooler for Fooling Thermal Infrared Detectors
Hao Li†, Fanggao Wan, Yue Su, Yue Wu, Mingyang Zhang, Maoguo Gong
Historically, infrared adversarial attacks were single-use and tough to deploy. Using TEC, we implemented efficient attacks adaptable to hardware scenarios.
AAAI 2025   [paper]

Projects

U_pre
U-pre: U-Net is an excellent learner for time series forecasting
Time series forecasting is suited for U-Net's architecture due to its consistent input-output distributions and strong mathematical alignment. Combining U-Net with Bert-Encoder improved performance by incorporating both local and global attention.
[code] [report-cn]


M_pre
M-pre: Mamba for time series forecasting
We tried Mamba for time series forecasting based on feature-conditioned tokens, which outpreformed transformer-based U-pre.
[code] [report-cn]
crosstalk
AgentCrossTalk: Performe a Crsosstalk between two LLM agents
This project uses the Google Gemini to create a simple chatbot application simulating two crosstalk performers performing based on user-provided topics.
[code] [website]


FGSM3D
FGSM3D: Is the point cloud gradient perturbation attack feasible?
We tried to extend FGSM to the 3D field and achieved significant success within a certain gradient range, but the sampling method of 3D models tells us that things seem to be not that simple...
[code] [report-cn]