Junyan Li

Junyan Li

PhD student at UMass Amherst

I am currently a first-year PhD student in computer science at the University of Massachusetts Amherst, advised by Prof. Chuang Gan. Previously, I was an undergraduate at Zhejiang University and the University of Illinois Urbana-Champaign.

My research interest lies in multimodal foundation model and embodied AI.

🔥 News

  • 2024.01 CoVLM is accepted by ICLR2024. Check our Project Page and also GitHubimg.
  • 2023.07 EfficientViT is accepted by ICCV2023. Check it on GitHubimg.
  • 2023.06 ToP is accepted by KDD2023. Check it on GitHubimg.
  • 2023.03 ProxylessGaze is publicly available as an application of ProxylessNASimg. It is an open-source gaze estimation pipeline including face detection, facial landmark detection and gaze estimation, running in real time on Raspberry Pi 4, Qualcomm GPU and Intel CPU.

📝 Publications

ICLR2024
covlm_poster

CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding

Junyan Li, Delin Chen, Yining Hong, Zhenfang Chen, Peihao Chen, Yikang Shen, Chuang Gan

GitHub Project Page
  • CoVLM is specifically designed to guide the VLM to explicitly compose visual entities and relationships among the text and dynamically communicate with the vision encoder and detection network to achieve vision-language communicative decoding. It boosts the compositional reasoning ability of VLMs and achieve SoTA performance on various tasks involving compositional analysis.
ICCV2023
efficientvit_poster

EfficientViT: Lightweight Multi-Scale Attention for On-Device Semantic Segmentation

Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han

GitHub Poster
  • EfficientViT is a new family of vision models for efficient high-resolution vision, especially segmentation. The core building block of EfficientViT is a new lightweight multi-scale attention module that achieves global receptive field and multi-scale learning with only hardware-efficient operations.
KDD2023
top_poster

Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference

Junyan Li, Li Lyna Zhang, Jiahang Xu, Yujing Wang, Shaoguang Yan, Yunqing Xia, Yuqing Yang, Ting Cao, Hao Sun, Weiwei Deng, Qi Zhang, Mao Yang

GitHub Poster
  • ToP is a deployment friendly token pruning solution for Transformers.

🎖 Honors and Awards

  • 2022.11 Zhejiang Provincial Scholarship
  • 2022.11 Zhejiang University Second Scholarship
  • 2020.11 Zhejiang University Second Scholarship

📖 Educations

  • 2023.09 - (now), PhD, computer science, University of Massachusetts Amherst.
  • 2019.09 - 2023.06, Undergraduate, computer engineering, University of Illinois Urbana-Champaign.
  • 2019.09 - 2023.06, Undergraduate, computer engineering, Zhejiang University.

💻 Internships