activity
20152023
most citedSubject-driven Text-to-Image Generation via Apprenticeship Learning

46 citations · 129 across the 10 of their papers we have counts for

collaborators
Showing 2023Show all

6 papers · 1 filter

cs.CV2023

Controllable One-Shot Face Video Synthesis With Semantic Aware Prior

Kangning Liu, Yu-Chuan Su, Wei +3

The one-shot talking-head synthesis task aims to animate a source image to another pose and expression, which is dictated by a driving frame. Recent methods rely on warping the app…

cs.CV2023★ 5 cited

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…

cs.CV2023★ 21 cited

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…

cs.CV2023

Federated Learning of Shareable Bases for Personalization-Friendly Image Classification

Hong-You Chen, Jike Zhong, Mingda Zhang +5

Personalized federated learning (PFL) aims to harness the collective wisdom of clients' data while building personalized models tailored to individual clients' data distributions.…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 46 cited

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''…