most citedUnsupervised Discovery of Semantic Latent Directions in Diffusion Models

5 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023

Sequential Data Generation with Groupwise Diffusion Process

Sangyun Lee, Gayoung Lee, Hyunsu Kim +2

We present the Groupwise Diffusion Model (GDM), which divides data into multiple groups and diffuses one group at one time interval in the forward diffusion process. GDM generates…

cs.CV2023

Small Objects Matters in Weakly-supervised Semantic Segmentation

Cheolhyun Mun, Sanghuk Lee, Youngjung Uh +2

Weakly-supervised semantic segmentation (WSSS) performs pixel-wise classification given only image-level labels for training. Despite the difficulty of this task, the research comm…

cs.CV20235 cited

AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks

Kibeom Hong, Seogkyu Jeon, Junsoo Lee +6

To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corre…

cs.CV20235 cited

Unsupervised Discovery of Semantic Latent Directions in Diffusion Models

Yong-Hyun Park, Mingi Kwon, Junghyo Jo +1

Despite the success of diffusion models (DMs), we still lack a thorough understanding of their latent space. While image editing with GANs builds upon latent space, DMs rely on edi…

cs.CV2022

FurryGAN: High Quality Foreground-aware Image Synthesis

Jeongmin Bae, Mingi Kwon, Youngjung Uh

Foreground-aware image synthesis aims to generate images as well as their foreground masks. A common approach is to formulate an image as an masked blending of a foreground image a…