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Mengyao Lyu

I am currently a Ph.D. candidate at Tsinghua University, advised by Prof. Guiguang Ding. Prior to that, I was fortunate to work with Prof. Xiangzhi Bai and Prof. Hu Han. My research focuses on enhancing data efficiency and improving data explainability for computer vision algorithms.

Latest Publications 🙋🏻

Box-Level Active Detection
Mengyao Lyu, Jundong Zhou, Hui Chen, Yijie Huang, Dongdong Yu, Yaqian Li, Yandong Guo, Yuchen Guo, Liuyu Xiang, Guiguang Ding
CVPR, 2023 (Highlight, 2.5% acceptance rate)

One-dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
Mengyao Lyu*, Yuhong Yang*, Haiwen Hong, Hui Chen, Xuan Jin, Yuan He, Hui Xue, Jungong Han, Guiguang Ding
CVPR, 2024 (Highlight, 2.8% acceptance rate)

Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence
Mengyao Lyu*, Tianxiang Hao*, Xinhao Xu, Hui Chen, Zijia Lin, Jungong Han, Guiguang Ding
ECCV, 2024

EXPERIENCE

  • 2024.07 ~

    Tiktok

    Research on Multimodal Large Language Models

  • 2023.06 ~ 2024.03

    Alibaba

    Research on Controllable, Safe, and Fair Diffusion Generation
    • Precisely erased concepts from Diffusion models while maintaining safe concepts the same.
    • The obtained concept erasures facilitate training-free transfer and multi-concept customization.
    • Achieved SOTA results across ∼40 concepts, 7 Diffusion models and 4 erasing applications.

  • 2021.06 ~ 2022.11

    OPPO Research (Tsinghua-OPPO JCFDT)

    Research on Active Learning Algorithms for Data Closed-Loop in Object Detection
    • Proposed and implemented a novel active learning algorithm for object detection to improve data and training efficiency.
    • Reimplemented 10+ active detection baselines and SOTAs within a unified codebase for a fair evaluation.
    • Achieved SOTA results on public VOC, COCO and OPPO private datasets.

  • 2018.07 ~ 2018.08

    Horizon Robotics

    Research on Long-tailed Perception for Advanced Driver-Assistance System
    • Developed conditional generative adversarial networks to synthesize data for different road signs.
    • Achieved a 44% improvement in the accuracy of the road sign classification task.