96 citations · 352 across the 19 of their papers we have counts for
19 papers
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…
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…
Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry
Yong-Hyun Park, Mingi Kwon, Jaewoong Choi +2
Despite the success of diffusion models (DMs), we still lack a thorough understanding of their latent space. To understand the latent space , we analy…
Training-free Content Injection using h-space in Diffusion Models
Jaeseok Jeong, Mingi Kwon, Youngjung Uh
Diffusion models (DMs) synthesize high-quality images in various domains. However, controlling their generative process is still hazy because the intermediate variables in the proc…
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…
BallGAN: 3D-aware Image Synthesis with a Spherical Background
Minjung Shin, Yunji Seo, Jeongmin Bae +4
3D-aware GANs aim to synthesize realistic 3D scenes such that they can be rendered in arbitrary perspectives to produce images. Although previous methods produce realistic images,…
Diffusion Models already have a Semantic Latent Space
Mingi Kwon, Jaeseok Jeong, Youngjung Uh
Diffusion models achieve outstanding generative performance in various domains. Despite their great success, they lack semantic latent space which is essential for controlling the…
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…
LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data
Jihye Park, Sunwoo Kim, Soohyun Kim +4
Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability of handling mu…
Feature Statistics Mixing Regularization for Generative Adversarial Networks
Junho Kim, Yunjey Choi, Youngjung Uh
In generative adversarial networks, improving discriminators is one of the key components for generation performance. As image classifiers are biased toward texture and debiasing i…
Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing
Hyunsu Kim, Yunjey Choi, Junho Kim +2
Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing re…
ArrowGAN : Learning to Generate Videos by Learning Arrow of Time
Kibeom Hong, Youngjung Uh, Hyeran Byun
Training GANs on videos is even more sophisticated than on images because videos have a distinguished dimension: time. While recent methods designed a dedicated architecture consid…
In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization
Minsong Ki, Youngjung Uh, Wonyoung Lee +1
Weakly supervised object localization (WSOL) aims to localize the target object using only the image-level supervision. Recent methods encourage the model to activate feature maps…
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
Byeongho Heo, Sanghyuk Chun, Seong Joon Oh +5
Normalization techniques are a boon for modern deep learning. They let weights converge more quickly with often better generalization performances. It has been argued that the norm…
Rethinking the Truly Unsupervised Image-to-Image Translation
Kyungjune Baek, Yunjey Choi, Youngjung Uh +2
Every recent image-to-image translation model inherently requires either image-level (i.e. input-output pairs) or set-level (i.e. domain labels) supervision. However, even set-leve…
Reliable Fidelity and Diversity Metrics for Generative Models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh +2
Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated im…
StarGAN v2: Diverse Image Synthesis for Multiple Domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo +1
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) s…
Background Suppression Network for Weakly-supervised Temporal Action Localization
Pilhyeon Lee, Youngjung Uh, Hyeran Byun
Weakly-supervised temporal action localization is a very challenging problem because frame-wise labels are not given in the training stage while the only hint is video-level labels…
Photorealistic Style Transfer via Wavelet Transforms
Jaejun Yoo, Youngjung Uh, Sanghyuk Chun +2
Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by spatial distortions or unre…