6 citations · 24 across the 44 of their papers we have counts for
3 papers · 1 filter
MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation
Yanwu Yang, Guinan Su, Jiesi Hu +3
Universal medical image segmentation models have emerged as a promising paradigm due to their strong generalizability across diverse tasks, showing great potential for a wide range…
Measuring Style Similarity in Diffusion Models
Gowthami Somepalli, Anubhav Gupta, Kamal Gupta +5
Generative models are now widely used by graphic designers and artists. Prior works have shown that these models remember and often replicate content from their training data durin…
What do we learn from inverting CLIP models?
Hamid Kazemi, Atoosa Chegini, Jonas Geiping +2
We employ an inversion-based approach to examine CLIP models. Our examination reveals that inverting CLIP models results in the generation of images that exhibit semantic alignment…