Chen Wei

Research Interest

My primary areas of interest in research are computer vision and machine learning. Most of our understanding of the world stems from interpreting complex sensory signals, particularly visual information, yet turning this raw data into meaningful knowledge remains a fundamental challenge in artificial intelligence. My research aims to develop visual intelligence that can perceive, process and reason about visual signals with generality and scalability.

Lab Openings

I seek highly motivated candidates with strong technical backgrounds in AI/ML who share our passion for advancing visual intelligence, particularly through the following three directions:

Generative Understanding: Constructing and learning from generative models of the world.

Multimodal Learning: Connecting visual representations to commonsense knowledge base like LLMs to achieve higher-order cognition.

Self-Supervised Learning: Discovering structures in raw data to obtain scalable structured representations in wide domains of images and videos, and applicable to a variety of tasks.

2025 Fall PhD Applicants: Please submit your application to the Rice CS PhD program, due Jan. 1, 2025 with no application fee, and mention me as one of your Faculty of Interest. While I will review all relevant applications, contacting me after submission through email can help flag your application.

Rice Students: If you are a Rice student looking for potential advisors, please email me directly.

	GenEx: Generating an Explorable World , Tianmin Shu, Junfei Xiao, Luoxin Ye, Jiahao Wang, Cheng Peng, Chen Wei, Daniel Khashabi, Rama Chellappa, Alan Yuille, Jieneng Chen Technical report, 2024 arXiv / genex.world / (demos)
	De-Diffusion Makes Text a Strong Cross-Modal Interface Chen Wei, Chenxi Liu, Siyuan Qiao, Zhishuai Zhang, Alan Yuille, Jiahui Yu CVPR, 2024 arXiv / project page
	Towards Generalizable Tumor Synthesis Qi Chen, Xiaoxi Chen, Haorui Song, Alan Yuille, Zhiwei Xiong, Chen Wei, Zongwei Zhou CVPR, 2024 arXiv / code
	Unleashing the Power of Visual Prompting at the Pixel Level Junyang Wu, Xianhang Li, Chen Wei, Huiyu Wang, Alan Yuille, Yuyin Zhou, Cihang Xie, TMLR, 2024 arXiv / code
	Tuning LayerNorm in Attention: Towards Efficient MultiModal LLM Finetuning Bingchen Zhao, Haoqin Tu, Chen Wei, Jieru Mei, Cihang Xie, ICLR, 2024 Spotlight arXiv / huggingface
	Instruct2Attack: Language-Guided Semantic Adversarial Attacks Jiang Liu, Chen Wei, Yuxiang Guo, Heng Yu, Alan Yuille, Soheil Feizi, Chun Pong Lau, Rama Chellappa arXiv, 2023 arXiv
	Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics Haoqin Tu, Bingchen Zhao, Chen Wei, Cihang Xie, NeurIPS Instruction Workshop, 2023 arXiv / code
	Diffusion Models as Masked Autoencoders Chen Wei, Karttikeya Mangalam, Po-Yao Huang, Yanghao Li, Haoqi Fan, Hu Xu, Huiyu Wang, Cihang Xie, Alan Yuille, Christoph Feichtenhofer ICCV, 2023 arXiv / project page / Marktechpost
	SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-Training Yuanze Lin, Chen Wei, Huiyu Wang, Alan Yuille, Cihang Xie ICCV, 2023 arXiv
	Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, Chen Wei, Haoqi Fan, Po-Yao Huang, Vaibhav Aggarwal, Arkabandhu Chowdhury, Omid Poursaeed, Judy Hoffman, Jitendra Malik, Yanghao Li, Christoph Feichtenhofer* ICML, 2023 Oral arXiv / code
	Masked Autoencoders Enable Efficient Knowledge Distillers Yutong Bai, Zeyu Wang, Junfei Xiao, Chen Wei, Huiyu Wang, Alan Yuille, Yuyin Zhou, Cihang Xie CVPR, 2023 arXiv / code
	CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation Feng Wang, Huiyu Wang, Chen Wei, Alan Yuille, Wei Shen ECCV, 2022 arXiv / code
	In Defense of Image Pre-Training for Spatiotemporal Recognition Xianhang Li, Huiyu Wang, Chen Wei, Jieru Mei, Alan Yuille, Yuyin Zhou, Cihang Xie ECCV, 2022 arXiv / code
	Masked Feature Prediction for Self-Supervised Visual Pre-Training Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, Christoph Feichtenhofer CVPR, 2022 arXiv / code at pySlowFast Most Influential CVPR 2023 Papers
	iBOT: Image BERT Pre-Training with Online Tokenizer Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, Tao Kong ICLR, 2022 arXiv / code / press Improved and scaled up to the foundation model DINOv2 by Meta AI.
	CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning Chen Wei, Kihyuk Sohn, Clayton Mellina, Alan Yuille, Fan Yang CVPR, 2021 arXiv / code / poster / video
	CO2: Consistent Contrast for Unsupervised Visual Representation Learning Chen Wei, Huiyu Wang, Wei Shen, Alan Yuille ICLR, 2021 arXiv / Open Review / video
	Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning Chen Wei, Lingxi Xie, Xutong Ren, Yingda Xia, Chi Su, Jiaying Liu, Qi Tian, Alan Yuille CVPR, 2019 arXiv / code
	Deep Retinex Decomposition for Low-Light Enhancement Chen Wei, Wenjing Wang, Wenhan Yang, Jiaying Liu BMVC, 2018 Oral arXiv / code / project page & dataset #2 Most Cited BMVC Papers Over the Last Five Years
	GLADNet: Low-Light Enhancement Network with Global Awareness Wenjing Wang, Chen Wei, Wenhan Yang, Jiaying Liu FG Workshop, 2018 PDF / code / project page