Search Results for author: Jooyoung Choi

Found 13 papers, 6 papers with code

Toward Spatially Unbiased Generative Models

2 code implementations ICCV 2021 Jooyoung Choi, Jungbeom Lee, Yonghyun Jeong, Sungroh Yoon

From our observations, the generator's implicit positional encoding is translation-variant, making the generator spatially biased.

Denoising Image Generation +1

ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

1 code implementation ICCV 2021 Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, Sungroh Yoon

In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image.

Denoising Image Generation +2

FICGAN: Facial Identity Controllable GAN for De-identification

no code implementations2 Oct 2021 Yonghyun Jeong, Jooyoung Choi, Sungwon Kim, Youngmin Ro, Tae-Hyun Oh, Doyeon Kim, Heonseok Ha, Sungroh Yoon

In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility.

Attribute De-identification

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation

1 code implementation NeurIPS 2021 Jungbeom Lee, Jooyoung Choi, Jisoo Mok, Sungroh Yoon

Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object.

Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

Korean-Specific Dataset for Table Question Answering

1 code implementation LREC 2022 Changwook Jun, Jooyoung Choi, Myoseop Sim, Hyun Kim, Hansol Jang, Kyungkoo Min

Subsequently, we then build a pre-trained language model based on Transformer and fine-tune the model for table question answering with these datasets.

Language Modelling Question Answering +1

ANNA: Enhanced Language Representation for Question Answering

no code implementations28 Mar 2022 Changwook Jun, Hansol Jang, Myoseop Sim, Hyun Kim, Jooyoung Choi, Kyungkoo Min, Kyunghoon Bae

Pre-trained language models have brought significant improvements in performance in a variety of natural language processing tasks.

Language Modelling Question Answering

Perception Prioritized Training of Diffusion Models

5 code implementations CVPR 2022 Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, Sungroh Yoon

Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i. e., denoising score matching loss.

Denoising

Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models

no code implementations25 May 2023 Jooyoung Choi, Yunjey Choi, Yunji Kim, Junho Kim, Sungroh Yoon

Text-to-image diffusion models can generate diverse, high-fidelity images based on user-provided text prompts.

text-guided-image-editing

Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection

no code implementations30 May 2023 Daegyu Kim, Chaehun Shin, Jooyoung Choi, Dahuin Jung, Sungroh Yoon

Diffusion-Stego achieved a high capacity of messages (3. 0 bpp of binary messages with 98% accuracy, and 6. 0 bpp with 90% accuracy) as well as high quality (with a FID score of 2. 77 for 1. 0 bpp on the FFHQ 64$\times$64 dataset) that makes it challenging to distinguish from real images in the PNG format.

Denoising Image Generation

ControlDreamer: Stylized 3D Generation with Multi-View ControlNet

no code implementations2 Dec 2023 Yeongtak Oh, Jooyoung Choi, Yongsung Kim, MinJun Park, Chaehun Shin, Sungroh Yoon

Recent advancements in text-to-3D generation have significantly contributed to the automation and democratization of 3D content creation.

text-guided-generation Text to 3D

Improving Diffusion-Based Generative Models via Approximated Optimal Transport

1 code implementation8 Mar 2024 Daegyu Kim, Jooyoung Choi, Chaehun Shin, Uiwon Hwang, Sungroh Yoon

Our approach aims to approximate and integrate optimal transport into the training process, significantly enhancing the ability of diffusion models to estimate the denoiser outputs accurately.

Image Generation

Efficient Diffusion-Driven Corruption Editor for Test-Time Adaptation

no code implementations16 Mar 2024 Yeongtak Oh, Jonghyun Lee, Jooyoung Choi, Dahuin Jung, Uiwon Hwang, Sungroh Yoon

To address this, we propose a novel TTA method by leveraging a latent diffusion model (LDM) based image editing model and fine-tuning it with our newly introduced corruption modeling scheme.

Data Augmentation Test-time Adaptation

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