4 papers · 1 filter
Mass Concept Erasure in Diffusion Models with Concept Hierarchy
Jiahang Tu, Ye Li, Yiming Wu +3
The success of diffusion models has raised concerns about the generation of unsafe or harmful content, prompting concept erasure approaches that fine-tune modules to suppress speci…
CE-SDWV: Effective and Efficient Concept Erasure for Text-to-Image Diffusion Models via a Semantic-Driven Word Vocabulary
Jiahang Tu, Qian Feng, Jiahua Dong +4
Large-scale text-to-image (T2I) diffusion models have achieved remarkable generative performance about various concepts. With the limitation of privacy and safety in practice, the…
TextToucher: Fine-Grained Text-to-Touch Generation
Jiahang Tu, Hao Fu, Fengyu Yang +3
Tactile sensation plays a crucial role in the development of multi-modal large models and embodied intelligence. To collect tactile data with minimal cost as possible, a series of…
DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous Driving
Jiahang Tu, Wei Ji, Hanbin Zhao +3
In autonomous driving, deep models have shown remarkable performance across various visual perception tasks with the demand of high-quality and huge-diversity training datasets. Su…