Liwei Wang

I am an Assistant Professor in Computer Science and Engineering department at The Chinese University of Hong Kong (CUHK). Before coming to HK, I have worked for more than two years as a Senior Researcher in Tencent AI Lab at Bellevue, US.

I got my PhD from Computer Science Department, University of Illinois at Urbana-Champaign, advised by Prof. Svetlana Lazebnik. Here is my Short Bio.

The Language and Vision (LaVi) Lab, which I founded at the Department of Computer Science and Engineering at CUHK, conducts research in Natural Language Processing (NLP) and Computer Vision, with a particular emphasis on the intersection of vision and language. Our work encompasses a range of topics including Language+Vision, Large Language Models, Multi-modal Large Models, and Embodied AI. If you want to join LaVi Lab, please send an email to lwwang@cse.cuhk.edu.hk

Email / Google Scholar / Publications / Lab website (soon) /

• 2024/09:
  We are hiring Researchers, Interns, Postdocs and Phd students to work on topics of Embodied AI, Vision+Language, LLMs, multi-modal LLMs, and diffusion models.
• 2024/09:
  Will serve as an Area Chair of CVPR 2025.
• 2024/05:
  Will serve as an Area Chair of NeurIPS 2024.
• 2023/10:
  Our work on LLMs Preference Model have been accepted to EMNLP 2023!
• 2023/10:
  Our CLEVA, the comprehevsive Chinese Language Model Evalution Platform, has been accepted to EMNLP 2023 System Demonstration!
• 2023/07:
  I was invited to give a talk in the IAS Workshop on Mathematical Theory for Emergent Intelligence.
• 2023/07:
  Our work on Vision-Language Parameter-Efficient Tuning has been accepted to ICCV 2023.
• 2023/07:
  Our work on reasoning with LLMs has been accepted to ACL 2023.
• 2023/06:
  Will serve as an Area Chair of CVPR 2024
• 2022/10:
  LaVi's two EMNLP 2022 Long papers on Dialogue research are accepted!
• 2022/10:
  Serving as an Area Chair of CVPR 2023
• 2022/07:
  Our LaVi team won the annual NLP challenge LIC 2022 (Multi-modal Video Understanding track) hosted by CCF and CIPS, check department news here .
• 2022/07:
  One ECCV paper is accepted
• 2022/03:
  Three papers got accepted to CVPR 2022 including our new work on Language + Vision.
• 2022/02:
  One ACL 2022 long paper from our group on "Probing Pre-trained Models" has bee accepted.
• 2022/02:
  Serving as an Area Chair of ECCV 2022 .

My students or interns are indicated by '*'. Click full publication list

Towards Learning a Generalist Model for Embodied Navigation

Making Long-Context Language Models Better Multi-Hop Reasoners

Beyond Embeddings: The Promise of Visual Table in Multi-Modal Models

Enhancing Temporal Modeling of Video LLMs via Time Gating

Learning Preference Model for LLMs via Automatic Preference Data Generation

CLEVA: Chinese Language Models EVAluation Platform

VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control

Multi-View Transformer for 3D Visual Grounding

Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation

FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows

Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency

SAT: 2D Semantics Assisted Training for 3D Visual Grounding

Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation

Robust Dialogue Utterance Rewriting as Sequence Tagging

Comprehensive Image Captioning via Scene Graph Decomposition

Improving One-stage Visual Grounding by Recursive Sub-query Construction

MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning

A Fast and Accurate One-Stage Approach to Visual Grounding​

Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech

Learning Two-Branch Neural Networks for Image-Text Matching Tasks

Learning structural motif representations for efficient protein structure search

Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space

Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models

Learning Deep Structure-Preserving Image-Text Embeddings

(Templates adapted from Arun's webpage.)