23 citations · 64 across the 20 of their papers we have counts for
23 papers
Beyond User-Side Defenses: Image Protection in Open-Weight Diffusion Models
Jihye Kang, Jiwon Park, Namhyuk Ahn
Model providers can support image protection, but open-weight deployment introduces practical constraints. We propose four requirements: protection effectiveness, clean fidelity, c…
Mean Accuracy Is the Wrong Estimand for a Release Decision
Jaewon Choi, Jihye Kang, Namhyuk Ahn
A release decision about a fine-tuned model is an operational commitment about the \emph{next} rerun of its training recipe. Yet standard benchmark reporting gives only the mean ac…
StyleComposer: Training-Free Multi-Reference Style Composition
Sanghyeok Lee, Jihye Kang, Namhyuk Ahn
The style of a painting is not monolithic: color, texture, and structure may come from different sources. Existing reference-guided methods transfer them as one style signal, leavi…
Compositional Image Synthesis with Inference-Time Scaling
Minsuk Ji, Sanghyeok Lee, Namhyuk Ahn
Despite their impressive realism, modern text-to-image models still struggle with compositionality, often failing to render accurate object counts, attributes, and spatial relation…
Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models
Namhyuk Ahn, KiYoon Yoo, Wonhyuk Ahn +2
Recent advancements in diffusion models revolutionize image generation but pose risks of misuse, such as replicating artworks or generating deepfakes. Existing image protection met…
Imperceptible Protection against Style Imitation from Diffusion Models
Namhyuk Ahn, Wonhyuk Ahn, KiYoon Yoo +2
Recent progress in diffusion models has profoundly enhanced the fidelity of image generation, but it has raised concerns about copyright infringements. While prior methods have int…
DreamStyler: Paint by Style Inversion with Text-to-Image Diffusion Models
Namhyuk Ahn, Junsoo Lee, Chunggi Lee +4
Recent progresses in large-scale text-to-image models have yielded remarkable accomplishments, finding various applications in art domain. However, expressing unique characteristic…
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…
Magnitude Attention-based Dynamic Pruning
Jihye Back, Namhyuk Ahn, Jangho Kim
Existing pruning methods utilize the importance of each weight based on specified criteria only when searching for a sparse structure but do not utilize it during training. In this…
LPMM: Intuitive Pose Control for Neural Talking-Head Model via Landmark-Parameter Morphable Model
Kwangho Lee, Patrick Kwon, Myung Ki Lee +2
While current talking head models are capable of generating photorealistic talking head videos, they provide limited pose controllability. Most methods require specific video seque…
DiffBlender: Composable and Versatile Multimodal Text-to-Image Diffusion Models
Sungnyun Kim, Junsoo Lee, Kibeom Hong +2
In this study, we aim to enhance the capabilities of diffusion-based text-to-image (T2I) generation models by integrating diverse modalities beyond textual descriptions within a un…
Interactive Cartoonization with Controllable Perceptual Factors
Namhyuk Ahn, Patrick Kwon, Jihye Back +2
Cartoonization is a task that renders natural photos into cartoon styles. Previous deep cartoonization methods only have focused on end-to-end translation, which may hinder editabi…
WebtoonMe: A Data-Centric Approach for Full-Body Portrait Stylization
Jihye Back, Seungkwon Kim, Namhyuk Ahn
Full-body portrait stylization, which aims to translate portrait photography into a cartoon style, has drawn attention recently. However, most methods have focused only on converti…
Cross-Domain Style Mixing for Face Cartoonization
Seungkwon Kim, Chaeheon Gwak, Dohyun Kim +4
Cartoon domain has recently gained increasing popularity. Previous studies have attempted quality portrait stylization into the cartoon domain; however, this poses a great challeng…
What is Wrong with One-Class Anomaly Detection?
JuneKyu Park, Jeong-Hyeon Moon, Namhyuk Ahn +1
From a safety perspective, a machine learning method embedded in real-world applications is required to distinguish irregular situations. For this reason, there has been a growing…
Restoring Spatially-Heterogeneous Distortions using Mixture of Experts Network
Sijin Kim, Namhyuk Ahn, Kyung-Ah Sohn
In recent years, deep learning-based methods have been successfully applied to the image distortion restoration tasks. However, scenarios that assume a single distortion only may n…
From Attack to Protection: Leveraging Watermarking Attack Network for Advanced Add-on Watermarking
Seung-Hun Nam, Jihyeon Kang, Daesik Kim +2
Multi-bit watermarking (MW) has been designed to enhance resistance against watermarking attacks, such as signal processing operations and geometric distortions. Various benchmark…
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr, Martin Danelljan, Radu Timofte +43
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world settin…
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution
Namhyuk Ahn, Jaejun Yoo, Kyung-Ah Sohn
In this paper, we tackle a fully unsupervised super-resolution problem, i.e., neither paired images nor ground truth HR images. We assume that low resolution (LR) images are relati…
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn
Data augmentation is an effective way to improve the performance of deep networks. Unfortunately, current methods are mostly developed for high-level vision tasks (e.g., classifica…
Efficient Deep Neural Network for Photo-realistic Image Super-Resolution
Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
Recent progress in deep learning-based models has improved photo-realistic (or perceptual) single-image super-resolution significantly. However, despite their powerful performance,…
Image Distortion Detection using Convolutional Neural Network
Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
Image distortion classification and detection is an important task in many applications. For example when compressing images, if we know the exact location of the distortion, then…
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks. Despite their great performances, deep learning methods cannot be easi…