Sangeek Hyun
I am a Ph.D. candidate in the Visual Computing Lab (VCLab) at Sungkyunkwan University, supervised by Prof. Jae-Pil Heo.
I received my Master's and Bachelor's degrees from Sungkyunkwan University.
My research interests include various tasks in machine learning and computer vision, with a particular focus on generative models and video understanding.
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Research
Recently my interest has been in 3D generative models using Generative Adversarial Networks and gaussian splatting. I am also interested in various generation tasks using large-scale diffusion models.
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GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats
Sangeek Hyun, Jae-Pil Heo
NeurIPS, 2024
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arXiv
First 3D GANs utilize gaussian splatting without any structural priors, achieving faster rendering speed at high-resolution data compared to NeRFs.
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Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer
Jiwoo Chung*, Sangeek Hyun*, Jae-Pil Heo (*: Equal contribution)
CVPR, 2024 (Highlight)
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arXiv
Training-free style transfer utilizes large-scale diffusion models by manipulating the attention features.
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Diversity-aware Channel Pruning for StyleGAN Compression
Jiwoo Chung, Sangeek Hyun, Sang-Heon Shim, Jae-Pil Heo
CVPR, 2024
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arXiv
GAN compression technique by pruning the diversity-aware channels in StyleGAN architecture.
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Task-disruptive Background Suppression for Few-Shot Segmentation
Suho Park, SuBeen Lee, Sangeek Hyun, Hyun Seok Seong, Jae-Pil Heo
AAAI, 2024
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arXiv
Task-disruptive Background Suppression mitigates the negative impact of dissimilar or target-similar support backgrounds, improving the accuracy of segmenting novel target objects in query images.
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Correlation-guided Query-Dependency Calibration in Video Representation Learning for Temporal Grounding
WonJun Moon, Sangeek Hyun, SuBeen Lee, Jae-Pil Heo
Arxiv, 2023
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arXiv
CG-DETR improves temporal grounding by using adaptive cross-attention and clip-word correlation to accurately identify video highlights corresponding to textual descriptions.
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Frequency-based motion representation for video generative adversarial networks
Sangeek Hyun, Jaihyun Lew, Jiwoo Chung, Euiyeon Kim, Jae-Pil Heo
TIP, 2023
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Propose a frequency-based motion representation for video GANs, enabling speed-aware motion generation, which improves video quality and editing capability.
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Disentangled Representation Learning for Unsupervised Neural Quantization
Haechan Noh, Sangeek Hyun, Woojin Jeong, Hanshin Lim, Jae-Pil Heo
CVPR, 2023
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Disentangled representation learning for unsupervised neural quantization addresses deep learning quantizers' limitations in leveraging residual vector space, enhancing search efficiency and quality.
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Query-dependent video representation for moment retrieval and highlight detection
WonJun Moon*, Sangeek Hyun*, SangUk Park, Dongchan Park, Jae-Pil Heo (*: Equal contribution)
CVPR, 2023
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arXiv
Query-Dependent DETR improves video moment retrieval and highlight detection by enhancing query-video relevance and using negative pairs to refine saliency prediction.
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Local attention pyramid for scene image generation
Sang-Heon Shim, Sangeek Hyun, DaeHyun Bae, Jae-Pil Heo
CVPR, 2022
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The Local Attention Pyramid (LAP) module addresses class-wise visual quality imbalance in GAN-generated scene images by enhancing attention to diverse object classes, particularly small and less frequent ones.
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Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency
Sangeek Hyun, Jihwan Kim, Jae-Pil Heo
CVPR, 2021
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Self-supervised approaches with dual discriminators improve video GANs by ensuring appearance consistency and motion coherency through contrastive learning and temporal structure puzzles.
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VarSR: Variational Super-Resolution Network for Very Low Resolution Images
Sangeek Hyun, Jae-Pil Heo
ECCV, 2020
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VarSR leverages latent distributions to address the many-to-one nature of single image super-resolution, generating diverse high-resolution images from low-resolution inputs.
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