10 citations · 21 across the 11 of their papers we have counts for
13 papers
BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing
Chaewon Park, Soyoon Lee, Naeun Lee +3
Real image editing enables precise manipulation of visual content, yet existing methods often fail in complex multi-object scenarios, causing semantic blending, object duplication,…
Exploiting Domain Properties in Language-Driven Domain Generalization for Semantic Segmentation
Seogkyu Jeon, Kibeom Hong, Hyeran Byun
Recent domain generalized semantic segmentation (DGSS) studies have achieved notable improvements by distilling semantic knowledge from Vision-Language Models (VLMs). However, they…
Directional Textual Inversion for Personalized Text-to-Image Generation
Kunhee Kim, NaHyeon Park, Kibeom Hong +1
Textual Inversion (TI) is an efficient approach to text-to-image personalization but often fails on complex prompts. We trace these failures to embedding norm inflation: learned to…
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong +2
Federated Learning (FL) enables privacy-preserving multi-source information fusion (MSIF) but is challenged by client drift in highly heterogeneous data settings. Many existing dri…
ConTEXTure: Consistent Multiview Images to Texture
Jaehoon Ahn, Sumin Cho, Harim Jung +3
We introduce ConTEXTure, a generative network designed to create a texture map/atlas for a given 3D mesh using images from multiple viewpoints. The process begins with generating a…
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…
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…
Exploiting Domain Transferability for Collaborative Inter-level Domain Adaptive Object Detection
Mirae Do, Seogkyu Jeon, Pilhyeon Lee +3
Domain adaptation for object detection (DAOD) has recently drawn much attention owing to its capability of detecting target objects without any annotations. To tackle the problem,…
Domain-Aware Universal Style Transfer
Kibeom Hong, Seogkyu Jeon, Huan Yang +2
Style transfer aims to reproduce content images with the styles from reference images. Existing universal style transfer methods successfully deliver arbitrary styles to original i…
Continuous Face Aging Generative Adversarial Networks
Seogkyu Jeon, Pilhyeon Lee, Kibeom Hong +1
Face aging is the task aiming to translate the faces in input images to designated ages. To simplify the problem, previous methods have limited themselves only able to produce disc…
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…