9 papers
UniTac: A Unified Multimodal Model for Cross-Sensor Tactile Understanding and Generation
Jiahang Tu, Fengyu Yang, Chenyang Ma +8
Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities. However, existing research rarely extends this para…
FG-OrIU: Towards Better Forgetting via Feature-Gradient Orthogonality for Incremental Unlearning
Qian Feng, JiaHang Tu, Mintong Kang +3
Incremental unlearning (IU) is critical for pre-trained models to comply with sequential data deletion requests, yet existing methods primarily suppress parameters or confuse knowl…
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…
IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting
Hao Fu, Hanbin Zhao, Jiahua Dong +3
Recent pre-trained vision-language models (PT-VLMs) often face a Multi-Domain Task Incremental Learning (MTIL) scenario in practice, where several classes and domains of multi-moda…
LW2G: Learning Whether to Grow for Prompt-based Continual Learning
Qian Feng, Da-wei Zhou, Hanbin Zhao +4
Recent Prompt-based Continual learning (PCL) has achieved remarkable performance with pre-trained models. These approaches expand a prompt pool by adding a new set of prompts while…