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Chaeyeon Chung

5 accepted papers

2025

Fair Generation without Unfair Distortions: Debiasing Text-to-Image Generation with Entanglement-Free Attention

ICCV 2025poster

Recent advancements in diffusion-based text-to-image (T2I) models have enabled the generation of high-quality and photorealistic images from text. However, they often exhibit societal biases related to gender, race, and socioeconomic status, thereby potentially reinforcing harmful stereotypes and sh…

Cited by 0SourcePDFScholar
2025

What to Preserve and What to Transfer: Faithful, Identity-Preserving Diffusion-based Hairstyle Transfer

AAAI 2025technical

Hairstyle transfer is a challenging task in the image editing field that modifies the hairstyle of a given face image while preserving its other appearance and background features. The existing hairstyle transfer approaches heavily rely on StyleGAN, which is pre-trained on cropped and aligned face i…

2024

Enhancing Intrinsic Features for Debiasing via Investigating Class-Discerning Common Attributes in Bias-Contrastive Pair

CVPR 2024poster

In the image classification task deep neural networks frequently rely on bias attributes that are spuriously correlated with a target class in the presence of dataset bias resulting in degraded performance when applied to data without bias attributes. The task of debiasing aims to compel classifiers…

Cited by 0SourcePDFScholar
2023

Shortcut-V2V: Compression Framework for Video-to-Video Translation Based on Temporal Redundancy Reduction

ICCV 2023poster

Video-to-video translation aims to generate video frames of a target domain from an input video. Despite its usefulness, the existing networks require enormous computations, necessitating their model compression for wide use. While there exist compression methods that improve computational efficienc…

Cited by 2PDFcodeScholar
2022

Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment

ECCV 2022poster

"Editing hairstyle is unique and challenging due to the complexity and delicacy of hairstyle. Although recent approaches significantly improved the hair details, these models often produce undesirable outputs when a pose of a source image is considerably different from that of a target hair image, l…