65 citations · 113 across the 8 of their papers we have counts for
8 papers
Dense Text-to-Image Generation with Attention Modulation
Yunji Kim, Jiyoung Lee, Jin-Hwa Kim +2
Existing text-to-image diffusion models struggle to synthesize realistic images given dense captions, where each text prompt provides a detailed description for a specific image re…
Context-Preserving Two-Stage Video Domain Translation for Portrait Stylization
Doyeon Kim, Eunji Ko, Hyunsu Kim +5
Portrait stylization, which translates a real human face image into an artistically stylized image, has attracted considerable interest and many prior works have shown impressive q…
Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models
Jooyoung Choi, Yunjey Choi, Yunji Kim +2
Text-to-image diffusion models can generate diverse, high-fidelity images based on user-provided text prompts. Recent research has extended these models to support text-guided imag…
Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models
Jaewoong Lee, Sangwon Jang, Jaehyeong Jo +5
Token-based masked generative models are gaining popularity for their fast inference time with parallel decoding. While recent token-based approaches achieve competitive performanc…
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models
Jin-Hwa Kim, Yunji Kim, Jiyoung Lee +2
Text-to-image generation and image captioning are recently emerged as a new experimental paradigm to assess machine intelligence. They predict continuous quantity accompanied by th…
Contrastive Fine-grained Class Clustering via Generative Adversarial Networks
Yunji Kim, Jung-Woo Ha
Unsupervised fine-grained class clustering is a practical yet challenging task due to the difficulty of feature representations learning of subtle object details. We introduce C3-G…
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction
Yunji Kim, Seonghyeon Nam, In Cho +1
We propose a deep video prediction model conditioned on a single image and an action class. To generate future frames, we first detect keypoints of a moving object and predict futu…
Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language
Seonghyeon Nam, Yunji Kim, Seon Joo Kim
This paper addresses the problem of manipulating images using natural language description. Our task aims to semantically modify visual attributes of an object in an image accordin…