7 papers · 1 filter
DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation
Héctor Laria, Yiping Han, Julian D. Santamaria +4
Adapting pre-trained text-to-image diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended effects alone. We argue thi…
Causal-Tune: Mining Causal Factors from Vision Foundation Models for Domain Generalized Semantic Segmentation
Yin Zhang, Yongqiang Zhang, Yaoyue Zheng +2
Fine-tuning Vision Foundation Models (VFMs) with a small number of parameters has shown remarkable performance in Domain Generalized Semantic Segmentation (DGSS). Most existing wor…
An h-space Based Adversarial Attack for Protection Against Few-shot Personalization
Xide Xu, Sandesh Kamath, Muhammad Atif Butt +1
The versatility of diffusion models in generating customized images from few samples raises significant privacy concerns, particularly regarding unauthorized modifications of priva…
Multi-label out-of-distribution detection via evidential learning
Eduardo Aguilar, Bogdan Raducanu, Petia Radeva
A crucial requirement for machine learning algorithms is not only to perform well, but also to show robustness and adaptability when encountering novel scenarios. One way to achiev…
Assessing Open-world Forgetting in Generative Image Model Customization
Héctor Laria, Alex Gomez-Villa, Kai Wang +2
Recent advances in diffusion models have significantly enhanced image generation capabilities. However, customizing these models with new classes often leads to unintended conseque…
Privacy Protection in Personalized Diffusion Models via Targeted Cross-Attention Adversarial Attack
Xide Xu, Muhammad Atif Butt, Sandesh Kamath +1
The growing demand for customized visual content has led to the rise of personalized text-to-image (T2I) diffusion models. Despite their remarkable potential, they pose significant…