114 citations · 132 across the 9 of their papers we have counts for
9 papers
Multi-Class Textual-Inversion Secretly Yields a Semantic-Agnostic Classifier
Kai Wang, Fei Yang, Bogdan Raducanu +1
With the advent of large pre-trained vision-language models such as CLIP, prompt learning methods aim to enhance the transferability of the CLIP model. They learn the prompt given…
LocInv: Localization-aware Inversion for Text-Guided Image Editing
Chuanming Tang, Kai Wang, Fei Yang +1
Large-scale Text-to-Image (T2I) diffusion models demonstrate significant generation capabilities based on textual prompts. Based on the T2I diffusion models, text-guided image edit…
Improving Continual Learning Performance and Efficiency with Auxiliary Classifiers
Filip Szatkowski, Yaoyue Zheng, Fei Yang +3
Continual learning is crucial for applying machine learning in challenging, dynamic, and often resource-constrained environments. However, catastrophic forgetting - overwriting pre…
Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
Kai Wang, Fei Yang, Shiqi Yang +2
Large-scale text-to-image generative models have been a ground-breaking development in generative AI, with diffusion models showing their astounding ability to synthesize convincin…
ScrollNet: Dynamic Weight Importance for Continual Learning
Fei Yang, Kai Wang, Joost van de Weijer
The principle underlying most existing continual learning (CL) methods is to prioritize stability by penalizing changes in parameters crucial to old tasks, while allowing for plast…
Attention Distillation: self-supervised vision transformer students need more guidance
Kai Wang, Fei Yang, Joost van de Weijer
Self-supervised learning has been widely applied to train high-quality vision transformers. Unleashing their excellent performance on memory and compute constraint devices is there…