activity
20222025
most citedME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning

23 citations · 29 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

5 papers · 1 filter

cs.CV2025

NSFW-Classifier Guided Prompt Sanitization for Safe Text-to-Image Generation

Yu Xie, Chengjie Zeng, Lingyun Zhang +1

The rapid advancement of text-to-image (T2I) models, such as Stable Diffusion, has enhanced their capability to synthesize images from textual prompts. However, this progress also…

cs.CV2024

Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision Localization

Lingyun Zhang, Yu Xie, Yanwei Fu +1

As large-scale diffusion models continue to advance, they excel at producing high-quality images but often generate unwanted content, such as sexually explicit or violent content.…

cs.CV2023★ 6 cited

StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning

Yuqian Fu, Yu Xie, Yanwei Fu +1

Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles few-shot learning across different domains. It aims at transferring prior knowledge learned on the…

cs.CV2022★ 23 cited

ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning

Yuqian Fu, Yu Xie, Yanwei Fu +2

Recently, Cross-Domain Few-Shot Learning (CD-FSL) which aims at addressing the Few-Shot Learning (FSL) problem across different domains has attracted rising attention. The core cha…

cs.CV2022

Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning

Yuqian Fu, Yu Xie, Yanwei Fu +2

Previous few-shot learning (FSL) works mostly are limited to natural images of general concepts and categories. These works assume very high visual similarity between the source an…