192 citations · 617 across the 27 of their papers we have counts for
6 papers · 1 filter
DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models
Shaoan Xie, Yang Zhao, Zhisheng Xiao +5
This study introduces Text-Guided Subject-Driven Image Inpainting, a novel task that combines text and exemplar images for image inpainting. While both text and exemplar images hav…
Identity Encoder for Personalized Diffusion
Yu-Chuan Su, Kelvin C. K. Chan, Yandong Li +5
Many applications can benefit from personalized image generation models, including image enhancement, video conferences, just to name a few. Existing works achieved personalization…
Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models
Xuhui Jia, Yang Zhao, Kelvin C. K. Chan +6
This paper proposes a method for generating images of customized objects specified by users. The method is based on a general framework that bypasses the lengthy optimization requi…
What's in a Name? Beyond Class Indices for Image Recognition
Kai Han, Xiaohu Huang, Yandong Li +3
Existing machine learning models demonstrate excellent performance in image object recognition after training on a large-scale dataset under full supervision. However, these models…
Subject-driven Text-to-Image Generation via Apprenticeship Learning
Wenhu Chen, Hexiang Hu, Yandong Li +4
Recent text-to-image generation models like DreamBooth have made remarkable progress in generating highly customized images of a target subject, by fine-tuning an ``expert model''…
Learning to Adapt to Online Streams with Distribution Shifts
Chenyan Wu, Yimu Pan, Yandong Li +1
Test-time adaptation (TTA) is a technique used to reduce distribution gaps between the training and testing sets by leveraging unlabeled test data during inference. In this work, w…