Search Results for author: Se Young Chun

Found 35 papers, 6 papers with code

Doubly Perturbed Task Free Continual Learning

no code implementations20 Dec 2023 Byung Hyun Lee, Min-hwan Oh, Se Young Chun

Specifically, for input perturbation, we propose an approximate perturbation method that injects noise into the input data as well as the feature vector and then interpolates the two perturbed samples.

Continual Learning Decision Making +1

Deep Internal Learning: Deep Learning from a Single Input

no code implementations12 Dec 2023 Tom Tirer, Raja Giryes, Se Young Chun, Yonina C. Eldar

Yet, in many cases there is value in training a network just from the input at hand.

Fast and accurate sparse-view CBCT reconstruction using meta-learned neural attenuation field and hash-encoding regularization

no code implementations4 Dec 2023 Heejun Shin, Taehee Kim, Jongho Lee, Se Young Chun, Seungryung Cho, Dongmyung Shin

In the FACT method, we meta-trained a neural network and a hash-encoder using a few scans (= 15), and a new regularization technique is utilized to reconstruct the details of an anatomical structure.

Detailed Human-Centric Text Description-Driven Large Scene Synthesis

no code implementations30 Nov 2023 Gwanghyun Kim, Dong Un Kang, Hoigi Seo, Hayeon Kim, Se Young Chun

Text-driven large scene image synthesis has made significant progress with diffusion models, but controlling it is challenging.

Image Generation Language Modelling +1

On Exact Inversion of DPM-Solvers

no code implementations30 Nov 2023 Seongmin Hong, Kyeonghyun Lee, Suh Yoon Jeon, Hyewon Bae, Se Young Chun

Diffusion probabilistic models (DPMs) are a key component in modern generative models.


Fully Quantized Always-on Face Detector Considering Mobile Image Sensors

no code implementations2 Nov 2023 Haechang Lee, Wongi Jeong, Dongil Ryu, Hyunwoo Je, Albert No, Kijeong Kim, Se Young Chun

In this study, we aim to bridge the gap by exploring extremely low-bit lightweight face detectors, focusing on the always-on face detection scenario for mobile image sensor applications.

Face Detection

Online Continual Learning on Hierarchical Label Expansion

no code implementations ICCV 2023 Byung Hyun Lee, Okchul Jung, Jonghyun Choi, Se Young Chun

To address this challenge, we propose a novel multi-level hierarchical class incremental task configuration with an online learning constraint, called hierarchical label expansion (HLE).

Continual Learning

Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensors

no code implementations ICCV 2023 Haechang Lee, Dongwon Park, Wongi Jeong, Kijeong Kim, Hyunwoo Je, Dongil Ryu, Se Young Chun

Our KLAP and KLAP-M methods achieved state-of-the-art demosaicing performance in both synthetic and real RAW data of Bayer and non-Bayer CFAs.

Demosaicking Meta-Learning

Neural Diffeomorphic Non-uniform B-spline Flows

1 code implementation7 Apr 2023 Seongmin Hong, Se Young Chun

In this work, we propose diffeomorphic non-uniform B-spline flows that are at least twice continuously differentiable while bi-Lipschitz continuous, enabling efficient parametrization while retaining analytic inverse transforms based on a sufficient condition for diffeomorphism.

DITTO-NeRF: Diffusion-based Iterative Text To Omni-directional 3D Model

no code implementations6 Apr 2023 Hoigi Seo, Hayeon Kim, Gwanghyun Kim, Se Young Chun

Our DITTO-NeRF consists of constructing high-quality partial 3D object for limited in-boundary (IB) angles using the given or text-generated 2D image from the frontal view and then iteratively reconstructing the remaining 3D NeRF using inpainting latent diffusion model.

3D Object Reconstruction Image to 3D +2

PODIA-3D: Domain Adaptation of 3D Generative Model Across Large Domain Gap Using Pose-Preserved Text-to-Image Diffusion

no code implementations ICCV 2023 Gwanghyun Kim, Ji Ha Jang, Se Young Chun

However, adapting 3D generators to domains with significant domain gaps from the source domain still remains challenging due to issues in current text-to-image diffusion models as following: 1) shape-pose trade-off in diffusion-based translation, 2) pose bias, and 3) instance bias in the target domain, resulting in inferior 3D shapes, low text-image correspondence, and low intra-domain diversity in the generated samples.

Domain Adaptation

All-in-One Image Restoration for Unknown Degradations Using Adaptive Discriminative Filters for Specific Degradations

no code implementations CVPR 2023 Dongwon Park, Byung Hyun Lee, Se Young Chun

Image restorations for single degradations have been widely studied, demonstrating excellent performance for each degradation, but can not reflect unpredictable realistic environments with unknown multiple degradations, which may change over time.

Image Restoration

On the Robustness of Normalizing Flows for Inverse Problems in Imaging

no code implementations ICCV 2023 Seongmin Hong, Inbum Park, Se Young Chun

Most normalizing flows for inverse problems in imaging employ the conditional affine coupling layer that can generate diverse images quickly.

Low-Light Image Enhancement Super-Resolution

DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model

no code implementations CVPR 2023 Gwanghyun Kim, Se Young Chun

Here we propose DATID-3D, a domain adaptation method tailored for 3D generative models using text-to-image diffusion models that can synthesize diverse images per text prompt without collecting additional images and camera information for the target domain.

3D Reconstruction Domain Adaptation

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

2 code implementations7 Nov 2022 Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen, Gen Li, Yuanfan Zhang, Lei Sun, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu, Ao Li, Lei Luo, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He

While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.

Image Super-Resolution

Coil2Coil: Self-supervised MR image denoising using phased-array coil images

no code implementations16 Aug 2022 Juhyung Park, Dongwon Park, Hyeong-Geol Shin, Eun-Jung Choi, Hongjun An, Minjun Kim, Dongmyung Shin, Se Young Chun, Jongho Lee

Hence, methods such as Noise2Noise (N2N) that require only pairs of noise-corrupted images have been developed to reduce the burden of obtaining training datasets.

Image Denoising

Adaptive GLCM sampling for transformer-based COVID-19 detection on CT

no code implementations4 Jul 2022 Okchul Jung, Dong Un Kang, Gwanghyun Kim, Se Young Chun

The experimental results show that the proposed method improve the detection performance with large margin without much difficult modification to the model.

Computed Tomography (CT)

Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging

no code implementations10 May 2022 Il Yong Chun, Dongwon Park, Xuehang Zheng, Se Young Chun, Yong Long

Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.

Image Denoising regression +1

Self-supervised regression learning using domain knowledge: Applications to improving self-supervised image denoising

no code implementations29 Sep 2021 Il Yong Chun, Dongwon Park, Xuehang Zheng, Se Young Chun, Yong Long

Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.

Image Denoising regression +1

Image Restoration by Deep Projected GSURE

no code implementations4 Feb 2021 Shady Abu-Hussein, Tom Tirer, Se Young Chun, Yonina C. Eldar, Raja Giryes

In the first one, where no explicit prior is used, we show that the proposed approach outperforms other internal learning methods, such as DIP.

Deblurring Image Restoration +1

Blur More To Deblur Better: Multi-Blur2Deblur For Efficient Video Deblurring

no code implementations23 Dec 2020 Dongwon Park, Dong Un Kang, Se Young Chun

Secondly, we propose multi-blurring recurrent neural network (MBRNN) that can synthesize more blurred images from neighboring frames, yielding substantially improved performance with existing video deblurring methods.

Ranked #5 on Deblurring on DVD (using extra training data)

Deblurring Image Deblurring

Task-Aware Variational Adversarial Active Learning

no code implementations CVPR 2021 Kwanyoung Kim, Dongwon Park, Kwang In Kim, Se Young Chun

Often, labeling large amount of data is challenging due to high labeling cost limiting the application domain of deep learning techniques.

Active Learning Generative Adversarial Network +1

Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training

1 code implementation ECCV 2020 Dongwon Park, Dong Un Kang, Jisoo Kim, Se Young Chun

Multi-scale (MS) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the output of lower scales.

Deblurring Image Deblurring

Down-Scaling with Learned Kernels in Multi-Scale Deep Neural Networks for Non-Uniform Single Image Deblurring

no code implementations25 Mar 2019 Dongwon Park, Jisoo Kim, Se Young Chun

Our proposed CNN-based down-scaling was the key factor for this excellent performance since the performance of our network without it was decreased by 1. 98dB.

Deblurring Image Deblurring

Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images

1 code implementation NeurIPS 2019 Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun

Here, we propose an extended SURE (eSURE) to train deep denoisers with correlated pairs of noise realizations per image and applied it to the case with two uncorrelated realizations per image to achieve better performance than SURE based method and comparable results to Noise2Noise.

Denoising Image Restoration

Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module

no code implementations19 Dec 2018 Dongwon Park, Yonghyeok Seo, Se Young Chun

However, rotation-invariance in robotic grasp detection has been only recently studied by using rotation anchor box that are often time-consuming and unreliable for multiple objects.

Face Detection Robotic Grasping

SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network

no code implementations18 Dec 2018 Kwanyoung Kim, Se Young Chun

Recently, SRGAN was proposed to avoid this average effect by minimizing perceptual losses instead of l1 loss and it yielded perceptually better SR images (or images with sharp edges) at the price of lowering PSNR.

Edge Detection Image Super-Resolution +1

Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images

no code implementations16 Sep 2018 Dongwon Park, Yonghyeok Seo, Se Young Chun

Our methods also achieved state-of-the-art detection accuracy (up to 96. 6%) with state-of- the-art real-time computation time for high-resolution images (6-20ms per 360x360 image) on Cornell dataset.

Training deep learning based image denoisers from undersampled measurements without ground truth and without image prior

no code implementations CVPR 2019 Magauiya Zhussip, Shakarim Soltanayev, Se Young Chun

Our proposed methods were able to train deep learning based image denoisers from undersampled measurements without ground truth images and without image priors, and to recover images with state-of-the-art qualities from undersampled data.

Compressive Sensing

Training Deep Learning Based Denoisers without Ground Truth Data

3 code implementations NeurIPS 2018 Shakarim Soltanayev, Se Young Chun

Our quick refining method outperformed conventional BM3D, deep image prior, and often the networks trained with ground truth.

Image Denoising Test

Classification based Grasp Detection using Spatial Transformer Network

no code implementations4 Mar 2018 Dongwon Park, Se Young Chun

Typically, regression based grasp detection methods have outperformed classification based detection methods in computation complexity with excellent accuracy.

Classification General Classification +1

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