Search Results for author: Yuanzhen Li

Found 7 papers, 2 papers with code

DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

4 code implementations25 Aug 2022 Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, Kfir Aberman

Once the subject is embedded in the output domain of the model, the unique identifier can then be used to synthesize fully-novel photorealistic images of the subject contextualized in different scenes.

Image Generation

Simplified Transfer Learning for Chest Radiography Models Using Less Data

1 code implementation Radiology 2022 Andrew B. Sellergren, Christina Chen, Zaid Nabulsi, Yuanzhen Li, Aaron Maschinot, Aaron Sarna, Jenny Huang, Charles Lau, Sreenivasa Raju Kalidindi, Mozziyar Etemadi, Florencia Garcia-Vicente, David Melnick, Yun Liu, Krish Eswaran, Daniel Tse, Neeral Beladia, Dilip Krishnan, Shravya Shetty

Supervised contrastive learning enabled performance comparable to state-of-the-art deep learning models in multiple clinical tasks by using as few as 45 images and is a promising method for predictive modeling with use of small data sets and for predicting outcomes in shifting patient populations.

Contrastive Learning Transfer Learning

LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery

no code implementations7 Jul 2022 Chun-Han Yao, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein, Ming-Hsuan Yang, Varun Jampani

In this work, we propose a practical problem setting to estimate 3D pose and shape of animals given only a few (10-30) in-the-wild images of a particular animal species (say, horse).

Deep Image-based Illumination Harmonization

no code implementations CVPR 2022 Zhongyun Bao, Chengjiang Long, Gang Fu, Daquan Liu, Yuanzhen Li, Jiaming Wu, Chunxia Xiao

Specifically, we firstly apply a physically-based rendering method to construct a large-scale, high-quality dataset (named IH) for our task, which contains various types of foreground objects and background scenes with different lighting conditions.

A Many-Objective Evolutionary Algorithm Based on Decomposition and Local Dominance

no code implementations13 Jul 2018 Yingyu Zhang, Yuanzhen Li, Quan-Ke Panb, P. N. Suganthan

Recent studies show that a well designed combination of the decomposition method and the domination method can improve the performance , i. e., convergence and diversity, of a MOEA.

A mullti- or many- objective evolutionary algorithm with global loop update

no code implementations25 Jan 2018 Yingyu Zhang, Bing Zeng, Yuanzhen Li, Junqing Li

The decomposition-based MOEAs emphasize convergence and diversity in a simple model and have made a great success in dealing with theoretical and practical multi- or many-objective optimization problems.

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