Greetings! I’m currently a PhD student at the University of Science and Technology of China. And I serve as a research intern at WeChat Vision, Tencent Inc.
My research interest includes:
- vision language models
- semantic segmentation
- parameter-efficient fine-tuning
I anticipate graduating in 2026 for industrial research positions. If you’re interested, please feel free to reach out to me via email. or WeChat (wzx-vi).
🔥 News
- 2025.06: 🔥 Delighted to announce that
HQCLIP: Leveraging Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models
were accepted by ICCV 2025! - 2024.09: 🔥 Delighted to announce that Masked Pre-trained Model Enables Universal Zero-shot Denoiser were accepted by NeurIPS 2024!
-
2024.02: 🔥 Rein is accepted in CVPR 2024! [Project Page]
- 2024.01: 🔥 Rein achieves SOTA in Cityscapes $\rightarrow$ ACDC test set generalization!
- 2023.10: 🔥 Rein is released and achieves SOTA in domain generalized semantic segmentation!
- 2023.07: 🎉 DTP is accepted in ICCV 2023 and achieves SOTA in night-time and full-time semantic segmentation!
- 2022.10: Our DDB receives the Spotlight Award in NeurIPS 2022!
- 2022.09: DDB is accepted in NeurIPS 2022 and achieves SOTA with ResNet counterparts on the single-source, multi-source, and multi-target domain-adaptive semantic segmentation tasks!
- 2022.03: A discriminator-free adversarial domain adaptation framework DALN is accepted in CVPR 2022!
📝 Publications
(* denotes equal contribution.)

First Author
HQCLIP: Leveraging Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models (arXiV comming soon) \
Zhixiang Wei*, Guangting Wang*, Xiaoxiao Ma, et al.
GitHub comming soon
- We generated detailed, bidirectional long-text descriptions for 1.3 billion images and pretrained/fine-tuned CLIP based on this dataset. Building upon this foundation, we propose a novel CLIP training framework that combines both bidirectional supervision and label classification losses. This framework achieves SoTA results on zero-shot classification, retrieval, and other tasks at the same data scale.

First Author
Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation
Zhixiang Wei*, Lin Chen*, Yi Jin*, Xiaoxiao Ma, et al.
- We propose the Reins framework, which efficiently fine-tunes vision foundation models for the domain generalized semantic segmentation (DGSS) task with just 1% trainable parameters, surprisingly surpassing full parameter fine-tuning. And Reins builds a new SOTA in various DGSS benchmarks.

First Author
Disentangle then Parse: Night-time Semantic Segmentation with Illumination Disentanglement
Zhixiang Wei*, Lin Chen*, et al.
- We propose a novel nigh-time semantic segmentation paradigm, i.e., disentangle then parse (DTP), which explicitly disentangles night-time images into light-invariant reflectance and light-specific illumination components and then recognizes semantics based on their adaptive fusion.

Co-First Author
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
Lin Chen*, Zhixiang Wei*, Xin Jin*, et al.
- We leverage the complementary characteristics of the coarse-wise and fine-wise data mixing techniques to progressively transfer the knowledge from the source to the target domain.

Co-First Author
Masked Pre-trained Model Enables Universal Zero-shot Denoiser
, Xiaoxiao Ma*, Zhixiang Wei*, et al.
- MPI is a zero-shot denoising pipeline designed for many types of noise degradations.

Co-Author
Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation
Lin Chen, Zhixiang Wei, Xin Jin, Enhong Chen.
- We reuse the category classifier as a discriminator to form a discriminator-free adversarial learning framework.
🎖 Honors and Awards
- 2023.10 National Scholarship Award(Top 1%)
- 2021~2023 The First Prize Scholarship of USTC for three consecutive years
- 2021.05 Outstanding Graduates of Anhui Province
📝 Academic Service (Reviewer)
- IEEE TPAMI
- IEEE TNNLS
- IEEE TCSVT
- IEEE/CVF CVPR
- NeurIPS
💬 Invited Talks
- 2024.09 IEEE ITSC Workshop: Foundation Models for Autonomous Driving, in Canada