4 papers · 1 filter
UNCAGE: Contrastive Attention Guidance for Masked Generative Transformers in Text-to-Image Generation
Wonjun Kang, Byeongkeun Ahn, Minjae Lee +4
Text-to-image (T2I) generation has been actively studied using Diffusion Models and Autoregressive Models. Recently, Masked Generative Transformers have gained attention as an alte…
Eta Inversion: Designing an Optimal Eta Function for Diffusion-based Real Image Editing
Wonjun Kang, Kevin Galim, Hyung Il Koo
Diffusion models have achieved remarkable success in the domain of text-guided image generation and, more recently, in text-guided image editing. A commonly adopted strategy for ed…
Counting Guidance for High Fidelity Text-to-Image Synthesis
Wonjun Kang, Kevin Galim, Hyung Il Koo +1
Recently, there have been significant improvements in the quality and performance of text-to-image generation, largely due to the impressive results attained by diffusion models. H…
Focus on defocus: bridging the synthetic to real domain gap for depth estimation
Maxim Maximov, Kevin Galim, Laura Leal-Taixé
Data-driven depth estimation methods struggle with the generalization outside their training scenes due to the immense variability of the real-world scenes. This problem can be par…