Kangfu MEI's Homepage

Kangfu MEI 梅康夫

I am a third-year Ph.D. student (2021 - 2025*) at Johns Hopkins University , advised by Prof. Vishal Patel , where I work on compute vision and computational photography. I got my M.Phil degree (2019 - 2021) at The Chinese University of Hong Kong, Shenzhen, advised by Prof. Rui Huang.

I am a Research Intern at Google Research (Jun 2023 - Now).

I previously interned at: Adobe Research / DAMO Academy / Kuaishou / JD.COM

CV / Email / Google Scholar / Github / Photography

Research

I have a strong interest in image, video, and 3D genetation. Representative papers are highlighted.

2024
	sDFT: Scaling Diffusion Field Transformers on Images, Videos, and 3D Data Kangfu Mei, Mo Zhou, Vishal M. Patel, Work in Progress PDF / project page A generative filed model that can generate image, video, and 3D data in a single unified network architecture. The unification can boost video generation with 3D prior in multi-task learning, with the 10 times smaller model size than SORA.
	Bigger is not Always Better: Scaling Properties of Latent Diffusion Models Kangfu Mei, Zhengzhong Tu, Mauricio Delbracio, Hossein Talebi, Vishal M. Patel, Peyman Milanfar arXiv 2024 arXiv The first work that throughly investigates the scaling properties of the recent trending latent diffsuion models (e.g., the representative StableDiffusion).
2023
	CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation Kangfu Mei, Mauricio Delbracio, Hossein Talebi, Zhengzhong Tu, Vishal M. Patel, Peyman Milanfar CVPR 2024 project page / arXiv Faster conditional diffusion that produces high-quality images with 1-4 sampling steps.
	VIDM: Video Implicit Diffusion Models Kangfu Mei, Vishal M. Patel, AAAI, 2023 Oral Presentation project page / arXiv Video Generation Diffusion Models By Using Implicit Motiion Condition.
2022
	Deep Semantic Statistics Matching (D2SM) Denoising Network Kangfu Mei, Vishal M. Patel, Rui Huang ECCV, 2022 project page / arXiv / poster A New General Plug-and-play Component For Denoising
	Latent Feature-Guided Diffusion Models for Shadow Removal Kangfu Mei, Luis Figueroa, Zhe Lin, Zhihong Ding, Scott Cohen, Vishal M. Patel WACV, 2023 project page / code / demo / arXiv We (together with Adobe) conducted this very early exploration of applying diffusion models on shadow removal. This proposes the first instance-level shadow removal task.
2021
	LTT-GAN: Looking Through Turbulence by Inverting GANs Kangfu Mei, Vishal M. Patel IEEE Journal of Selected Topics in Signal Processing [IF: 7.695] arXiv The first turbulence mitigation algorithm that can clearly recover face images captured in a range of 300 meters long.
	AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing Qi Song, Kangfu Mei, Rui Huang AAAI, 2021 code / arXiv Strip Attention Module (SAM) and Attention Fusion Module (AFM) are proposed for enhancing the accuracy of semantic segmentation networks with limited computational complexity.
2019
	Higher-resolution network for image demosaicing and enhancing Kangfu Mei, Juncheng Li, Jiajie Zhang, Haoyu Wu, Jie Li, Rui Huang ICCV AIM RAW to RGB mapping challenge, 2019 code / arXiv For the first time, a neural ISP has outperformed a traditional ISP (like Huawei's mobile ISP) and achieved visual quality comparable to that of a DSLR.
	Progressive Feature Fusion Network for Realistic Image Dehazing Kangfu Mei, Aiwen Jiang, Juncheng Li, Mingwen Wang ACCV, 2019 code / arXiv PFFNet was the first dehazing netowrk that uses fully end-to-end neural network architecture without physical gating unit. More than 100 works (untill 2024) adapot our strategy and show its effectiveness.
2018
	Multi-scale Residual Network for Image Super-resolution Juncheng Li, Faming Fang, Kangfu Mei, Guixu Zhang ECCV, 2018 code / bibtex We built MSRN in 2018. It quickly becomes the foundamental component in all image restoration works and has more than 800 citations untill 2024.