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1. HyperCLOVA X Technical Report

Author

Yoo, Kang Min, Han, Jaegeun, In, Sookyo, Jeon, Heewon, Jeong, Jisu, Kang, Jaewook, Kim, Hyunwook, Kim, Kyung-Min, Kim, Munhyong, Kim, Sungju, Kwak, Donghyun, Kwak, Hanock, Kwon, Se Jung, Lee, Bado, Lee, Dongsoo, Lee, Gichang, Lee, Jooho, Park, Baeseong, Shin, Seongjin, Yu, Joonsang, Baek, Seolki, Byeon, Sumin, Cho, Eungsup, Choe, Dooseok, Han, Jeesung, Jin, Youngkyun, Jun, Hyein, Jung, Jaeseung, Kim, Chanwoong, Kim, Jinhong, Kim, Jinuk, Lee, Dokyeong, Park, Dongwook, Sohn, Jeong Min, Han, Sujung, Heo, Jiae, Hong, Sungju, Jeon, Mina, Jung, Hyunhoon, Jung, Jungeun, Jung, Wangkyo, Kim, Chungjoon, Kim, Hyeri, Kim, Jonghyun, Kim, Min Young, Lee, Soeun, Park, Joonhee, Shin, Jieun, Yang, Sojin, Yoon, Jungsoon, Lee, Hwaran, Bae, Sanghwan, Cha, Jeehwan, Gylleus, Karl, Ham, Donghoon, Hong, Mihak, Hong, Youngki, Hong, Yunki, Jang, Dahyun, Jeon, Hyojun, Jeon, Yujin, Jeong, Yeji, Ji, Myunggeun, Jin, Yeguk, Jo, Chansong, Joo, Shinyoung, Jung, Seunghwan, Kim, Adrian Jungmyung, Kim, Byoung Hoon, Kim, Hyomin, Kim, Jungwhan, Kim, Minkyoung, Kim, Minseung, Kim, Sungdong, Kim, Yonghee, Kim, Youngjun, Kim, Youngkwan, Ko, Donghyeon, Lee, Dughyun, Lee, Ha Young, Lee, Jaehong, Lee, Jieun, Lee, Jonghyun, Lee, Jongjin, Lee, Min Young, Lee, Yehbin, Min, Taehong, Min, Yuri, Moon, Kiyoon, Oh, Hyangnam, Park, Jaesun, Park, Kyuyon, Park, Younghun, Seo, Hanbae, Seo, Seunghyun, Sim, Mihyun, Son, Gyubin, Yeo, Matt, Yeom, Kyung Hoon, Yoo, Wonjoon, You, Myungin, Ahn, Doheon, Ahn, Homin, Ahn, Joohee, Ahn, Seongmin, An, Chanwoo, An, Hyeryun, An, Junho, An, Sang-Min, Byun, Boram, Byun, Eunbin, Cha, Jongho, Chang, Minji, Chang, Seunggyu, Cho, Haesong, Cho, Youngdo, Choi, Dalnim, Choi, Daseul, Choi, Hyoseok, Choi, Minseong, Choi, Sangho, Choi, Seongjae, Choi, Wooyong, Chun, Sewhan, Go, Dong Young, Ham, Chiheon, Han, Danbi, Han, Jaemin, Hong, Moonyoung, Hong, Sung Bum, Hwang, Dong-Hyun, Hwang, Seongchan, Im, Jinbae, Jang, Hyuk Jin, Jang, Jaehyung, Jang, Jaeni, Jang, Sihyeon, Jang, Sungwon, Jeon, Joonha, Jeong, Daun, Jeong, Joonhyun, Jeong, Kyeongseok, Jeong, Mini, Jin, Sol, Jo, Hanbyeol, Jo, Hanju, Jo, Minjung, Jung, Chaeyoon, Jung, Hyungsik, Jung, Jaeuk, Jung, Ju Hwan, Jung, Kwangsun, Jung, Seungjae, Ka, Soonwon, Kang, Donghan, Kang, Soyoung, Kil, Taeho, Kim, Areum, Kim, Beomyoung, Kim, Byeongwook, Kim, Daehee, Kim, Dong-Gyun, Kim, Donggook, Kim, Donghyun, Kim, Euna, Kim, Eunchul, Kim, Geewook, Kim, Gyu Ri, Kim, Hanbyul, Kim, Heesu, Kim, Isaac, Kim, Jeonghoon, Kim, Jihye, Kim, Joonghoon, Kim, Minjae, Kim, Minsub, Kim, Pil Hwan, Kim, Sammy, Kim, Seokhun, Kim, Seonghyeon, Kim, Soojin, Kim, Soong, Kim, Soyoon, Kim, Sunyoung, Kim, Taeho, Kim, Wonho, Kim, Yoonsik, Kim, You Jin, Kim, Yuri, Kwon, Beomseok, Kwon, Ohsung, Kwon, Yoo-Hwan, Lee, Anna, Lee, Byungwook, Lee, Changho, Lee, Daun, Lee, Dongjae, Lee, Ha-Ram, Lee, Hodong, Lee, Hwiyeong, Lee, Hyunmi, Lee, Injae, Lee, Jaeung, Lee, Jeongsang, Lee, Jisoo, Lee, Jongsoo, Lee, Joongjae, Lee, Juhan, Lee, Jung Hyun, Lee, Junghoon, Lee, Junwoo, Lee, Se Yun, Lee, Sujin, Lee, Sungjae, Lee, Sungwoo, Lee, Wonjae, Lee, Zoo Hyun, Lim, Jong Kun, Lim, Kun, Lim, Taemin, Na, Nuri, Nam, Jeongyeon, Nam, Kyeong-Min, Noh, Yeonseog, Oh, Biro, Oh, Jung-Sik, Oh, Solgil, Oh, Yeontaek, Park, Boyoun, Park, Cheonbok, Park, Dongju, Park, Hyeonjin, Park, Hyun Tae, Park, Hyunjung, Park, Jihye, Park, Jooseok, Park, Junghwan, Park, Jungsoo, Park, Miru, Park, Sang Hee, Park, Seunghyun, Park, Soyoung, Park, Taerim, Park, Wonkyeong, Ryu, Hyunjoon, Ryu, Jeonghun, Ryu, Nahyeon, Seo, Soonshin, Seo, Suk Min, Shim, Yoonjeong, Shin, Kyuyong, Shin, Wonkwang, Sim, Hyun, Sim, Woongseob, Soh, Hyejin, Son, Bokyong, Son, Hyunjun, Son, Seulah, Song, Chi-Yun, Song, Chiyoung, Song, Ka Yeon, Song, Minchul, Song, Seungmin, Wang, Jisung, Yeo, Yonggoo, Yi, Myeong Yeon, Yim, Moon Bin, Yoo, Taehwan, Yoo, Youngjoon, Yoon, Sungmin, Yoon, Young Jin, Yu, Hangyeol, Yu, Ui Seon, Zuo, Xingdong, Bae, Jeongin, Bae, Joungeun, Cho, Hyunsoo, Cho, Seonghyun, Cho, Yongjin, Choi, Taekyoon, Choi, Yera, Chung, Jiwan, Han, Zhenghui, Heo, Byeongho, Hong, Euisuk, Hwang, Taebaek, Im, Seonyeol, Jegal, Sumin, Jeon, Sumin, Jeong, Yelim, Jeong, Yonghyun, Jiang, Can, Jiang, Juyong, Jin, Jiho, Jo, Ara, Jo, Younghyun, Jung, Hoyoun, Jung, Juyoung, Kang, Seunghyeong, Kim, Dae Hee, Kim, Ginam, Kim, Hangyeol, Kim, Heeseung, Kim, Hyojin, Kim, Hyojun, Kim, Hyun-Ah, Kim, Jeehye, Kim, Jin-Hwa, Kim, Jiseon, Kim, Jonghak, Kim, Jung Yoon, Kim, Rak Yeong, Kim, Seongjin, Kim, Seoyoon, Kim, Sewon, Kim, Sooyoung, Kim, Sukyoung, Kim, Taeyong, Ko, Naeun, Koo, Bonseung, Kwak, Heeyoung, Kwon, Haena, Kwon, Youngjin, Lee, Boram, Lee, Bruce W., Lee, Dagyeong, Lee, Erin, Lee, Euijin, Lee, Ha Gyeong, Lee, Hyojin, Lee, Hyunjeong, Lee, Jeeyoon, Lee, Jeonghyun, Lee, Jongheok, Lee, Joonhyung, Lee, Junhyuk, Lee, Mingu, Lee, Nayeon, Lee, Sangkyu, Lee, Se Young, Lee, Seulgi, Lee, Seung Jin, Lee, Suhyeon, Lee, Yeonjae, Lee, Yesol, Lee, Youngbeom, Lee, Yujin, Li, Shaodong, Liu, Tianyu, Moon, Seong-Eun, Moon, Taehong, Nihlenramstroem, Max-Lasse, Oh, Wonseok, Oh, Yuri, Park, Hongbeen, Park, Hyekyung, Park, Jaeho, Park, Nohil, Park, Sangjin, Ryu, Jiwon, Ryu, Miru, Ryu, Simo, Seo, Ahreum, Seo, Hee, Seo, Kangdeok, Shin, Jamin, Shin, Seungyoun, Sin, Heetae, Wang, Jiangping, Wang, Lei, Xiang, Ning, Xiao, Longxiang, Xu, Jing, Yi, Seonyeong, Yoo, Haanju, Yoo, Haneul, Yoo, Hwanhee, Yu, Liang, Yu, Youngjae, Yuan, Weijie, Zeng, Bo, Zhou, Qian, Cho, Kyunghyun, Ha, Jung-Woo, Park, Joonsuk, Hwang, Jihyun, Kwon, Hyoung Jo, Kwon, Soonyong, Lee, Jungyeon, Lee, Seungho, Lim, Seonghyeon, Noh, Hyunkyung, Choi, Seungho, Lee, Sang-Woo, Lim, Jung Hwa, and Sung, Nako

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs., Comment: 44 pages; updated authors list and fixed author names

Published
2024

9. NTIRE 2021 Challenge on Perceptual Image Quality Assessment

Author

Gu, Jinjin, Cai, Haoming, Dong, Chao, Ren, Jimmy S., Qiao, Yu, Gu, Shuhang, Timofte, Radu, Cheon, Manri, Yoon, Sungjun, Kang, Byungyeon, Lee, Junwoo, Zhang, Qing, Guo, Haiyang, Bin, Yi, Hou, Yuqing, Luo, Hengliang, Guo, Jingyu, Wang, Zirui, Wang, Hai, Yang, Wenming, Bai, Qingyan, Shi, Shuwei, Xia, Weihao, Cao, Mingdeng, Wang, Jiahao, Chen, Yifan, Yang, Yujiu, Li, Yang, Zhang, Tao, Feng, Longtao, Liao, Yiting, Li, Junlin, Thong, William, Pereira, Jose Costa, Leonardis, Ales, McDonagh, Steven, Xu, Kele, Yang, Lehan, Cai, Hengxing, Sun, Pengfei, Ayyoubzadeh, Seyed Mehdi, Royat, Ali, Fezza, Sid Ahmed, Hammou, Dounia, Hamidouche, Wassim, Ahn, Sewoong, Yoon, Gwangjin, Tsubota, Koki, Akutsu, Hiroaki, and Aizawa, Kiyoharu

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image processing technology, perceptual image processing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image processing algorithms and the corresponding subjective scores. Thus they can be used to develop and evaluate IQA methods on GAN-based distortions. The challenge has 270 registered participants in total. In the final testing stage, 13 participating teams submitted their models and fact sheets. Almost all of them have achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.

Published
2021

10. Perceptual Image Quality Assessment with Transformers

Author

Cheon, Manri, Yoon, Sung-Jun, Kang, Byungyeon, and Lee, Junwoo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

In this paper, we propose an image quality transformer (IQT) that successfully applies a transformer architecture to a perceptual full-reference image quality assessment (IQA) task. Perceptual representation becomes more important in image quality assessment. In this context, we extract the perceptual feature representations from each of input images using a convolutional neural network (CNN) backbone. The extracted feature maps are fed into the transformer encoder and decoder in order to compare a reference and distorted images. Following an approach of the transformer-based vision models, we use extra learnable quality embedding and position embedding. The output of the transformer is passed to a prediction head in order to predict a final quality score. The experimental results show that our proposed model has an outstanding performance for the standard IQA datasets. For a large-scale IQA dataset containing output images of generative model, our model also shows the promising results. The proposed IQT was ranked first among 13 participants in the NTIRE 2021 perceptual image quality assessment challenge. Our work will be an opportunity to further expand the approach for the perceptual IQA task., Comment: Accepted to NTIRE workshop at CVPR 2021. 1st Place in NTIRE 2021 perceptual IQA challenge. https://github.com/manricheon/IQT

Published
2021

11. Texture Transform Attention for Realistic Image Inpainting

Author

Kim, Yejin, Cheon, Manri, and Lee, Junwoo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Over the last few years, the performance of inpainting to fill missing regions has shown significant improvements by using deep neural networks. Most of inpainting work create a visually plausible structure and texture, however, due to them often generating a blurry result, final outcomes appear unrealistic and make feel heterogeneity. In order to solve this problem, the existing methods have used a patch based solution with deep neural network, however, these methods also cannot transfer the texture properly. Motivated by these observation, we propose a patch based method. Texture Transform Attention network(TTA-Net) that better produces the missing region inpainting with fine details. The task is a single refinement network and takes the form of U-Net architecture that transfers fine texture features of encoder to coarse semantic features of decoder through skip-connection. Texture Transform Attention is used to create a new reassembled texture map using fine textures and coarse semantics that can efficiently transfer texture information as a result. To stabilize training process, we use a VGG feature layer of ground truth and patch discriminator. We evaluate our model end-to-end with the publicly available datasets CelebA-HQ and Places2 and demonstrate that images of higher quality can be obtained to the existing state-of-the-art approaches.

Published
2020

12. NTIRE 2020 Challenge on Image Demoireing: Methods and Results

Author

Yuan, Shanxin, Timofte, Radu, Leonardis, Ales, Slabaugh, Gregory, Luo, Xiaotong, Zhang, Jiangtao, Qu, Yanyun, Hong, Ming, Xie, Yuan, Li, Cuihua, Xu, Dejia, Chu, Yihao, Sun, Qingyan, Liu, Shuai, Zong, Ziyao, Nan, Nan, Li, Chenghua, Kim, Sangmin, Nam, Hyungjoon, Kim, Jisu, Jeong, Jechang, Cheon, Manri, Yoon, Sung-Jun, Kang, Byungyeon, Lee, Junwoo, Zheng, Bolun, Liu, Xiaohong, Dai, Linhui, Chen, Jun, Cheng, Xi, Fu, Zhenyong, Yang, Jian, Lee, Chul, Vien, An Gia, Park, Hyunkook, Nathan, Sabari, Beham, M. Parisa, Roomi, S Mohamed Mansoor, Lemarchand, Florian, Pelcat, Maxime, Nogues, Erwan, Puthussery, Densen, S, Hrishikesh P, C V, Jiji, Sinha, Ashish, and Zhao, Xuan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. The challenge was divided into two tracks. Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image. Track 2 focused on the burst demoireing problem, where a set of degraded moire images of the same scene were provided as input, with the goal of producing a single demoired image as output. The methods were ranked in terms of their fidelity, measured using the peak signal-to-noise ratio (PSNR) between the ground truth clean images and the restored images produced by the participants' methods. The tracks had 142 and 99 registered participants, respectively, with a total of 14 and 6 submissions in the final testing stage. The entries span the current state-of-the-art in image and burst image demoireing problems.

Published
2020

17. Ocular Manifestation of Granulomatosis with Polyangiitis Presenting as Serous Retinal Detachment: A Case Report.

Author

Lee, Junwoo, Choi, Jaehwan, Yu, Seung-Young, and Kim, Kiyoung

Subjects
GRANULOMATOSIS with polyangiitis, RETINAL detachment, OPTICAL coherence tomography, EYE pain, OCULAR manifestations of general diseases
Abstract

Background: Ocular involvement is relatively common in granulomatosis with polyangiitis (GPA); however, choroidal involvement is rare. We present a case of serous retinal detachment resulting from choroidal involvement in GPA. Case presentation: A 55-year-old male presented to our clinic with bilateral eye redness and pain. Ocular examination revealed bilateral conjunctival injection, and dilation of the episcleral and scleral vessels. Slit-lamp examination revealed anterior chamber cells. Optical coherence tomography (OCT) confirmed serous retinal detachment (SRD) in the left eye. The patient had recently been diagnosed with GPA following a lung biopsy and had received immunosuppressive therapy, including systemic steroids, cyclosporine, mycophenolate mofetil, and rituximab. Five weeks after treatment, the clinical symptoms of the patient, including SRD, improved with alleviation of systemic symptoms. However, tapering systemic steroids and immunosuppressants and discontinuing rituximab led to disease reactivation. OCT demonstrated a recurrence of subretinal fluid, which had previously resolved, and slit-lamp examination showed mild bilateral engorged scleral vessels. Conclusions: Choroidal involvement can present as SRD and may indicate disease activity in patients with GPA. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Fluorinated Organic Cations Derived Chiral 2D Perovskite Enabling Enhanced Spin‐Dependent Oxygen Evolution Reaction.

Author

Son, Jaehyun, Jang, Gyumin, Ma, Sunihl, Lee, Hyungsoo, Lee, Chan Uk, Yang, Seongyeon, Lee, Junwoo, Moon, Subin, Jeong, Wooyong, Park, Jeong Hyun, Jung, Chan‐Woo, Kim, Ji‐Hee, Park, Ji‐Sang, and Moon, Jooho

Subjects
OXYGEN evolution reactions, CHARGE carriers, PEROVSKITE, FLUORINATION, OVERPOTENTIAL
Abstract

Chirality‐induced spin selectivity observed in chiral 2D organic–inorganic hybrid perovskite holds promise to achieve spin‐dependent electrochemistry. However, conventional chiral 2D perovskites suffer from low conductivity and hygroscopicity, limiting electrochemical performance and operational stability. Here, a cutting‐edge material design is introduced to develop a stable and efficient chiral perovskite‐based spin polarizer by employing fluorinated chiral cation. The fluorination approach effectively promotes the charge carrier transport along the out‐of‐plane direction by mitigating the dielectric confinement effect within the multi‐quantum well‐structured 2D perovskite. Integrating the fluorinated cation incorporated spin polarizer with BiVO4 photoanode considerably boosts the photocurrent density while reducing overpotential through a spin‐dependent oxygen evolution reaction. Furthermore, the hydrophobic nature of fluorine in spin polarizer endows operational stability to the photoanode, extending the durability by 280% as compared to the device with non‐fluorinated spin polarizer. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Bilateral Visual Impairment following Combination Chemotherapy with Carboplatin in Patients with Small Cell Lung Cancer: A Case Report.

Author

Kim, Jaeha, Lee, Junwoo, Lee, Seungyeon, and Kim, Kiyoung

Subjects
SMALL cell lung cancer, COMBINATION drug therapy, CARBOPLATIN, VISION disorders, CHOROID diseases, OCULAR toxicology, HAND-foot syndrome
Abstract

Background: Platinum-based combination chemotherapy, including cisplatin and carboplatin, are important cytotoxic anti-cancer agents that are widely used to treat various solid tumors. Carboplatin has a similar effect on survival in small cell lung cancer, but generally has a milder toxicity profile when compared with cisplatin. Both may cause moderate or severe neurotoxicity, but ocular neurotoxicity from carboplatin is rarely reported. Case presentation: A 79-year-old man underwent intravenous polychemotherapy (atezolizumab, etoposide, and carboplatin) for small cell lung cancer. One week after the second cycle of chemotherapy, he reported bilateral visual loss as hand motion in both eyes. Dilated fundus examination showed retinal arterial narrowing without hemorrhage, and diffuse choroidal and retinal thinning was observed in an optical coherence tomography scan. Fluorescein angiography revealed significantly delayed circulation without evidence of obstructive lesions. 30-Flicker electroretinogram testing showed a complete absence of cone response in both eyes. The patient's visual acuity aggravated to no light perception in both eyes, even after the cessation of chemotherapy. Conclusions: Carboplatin combination chemotherapy administered at therapeutic doses can result in irreversible visual loss, a side effect that is not widely acknowledged. When using carboplatin, physicians should be aware of its potential ocular toxicity. [ABSTRACT FROM AUTHOR]

Published
2024
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29. A Furan‐Substituted Polymeric Hole‐Transporting Material for Energy Level Regulation and Less Planarity in Colloidal Quantum Dot Solar Cells.

Author

Lee, Junwoo, Lee, Duck Hoon, Kim, Younghoon, Park, Jin Young, You, Hyung Ryul, Park, Taiho, Kim, Minjun, and Choi, Jongmin

Subjects
SEMICONDUCTOR nanocrystals, SOLAR cells, OPEN-circuit voltage, MOLECULAR orbitals, THERMAL stability, GLASS-ceramics
Abstract

For efficient colloidal quantum dot (CQD) solar cells (CQD‐SCs), thiol‐passivated p‐type CQDs are generally used as a hole‐transporting material (HTM); however, there are issues with the control of optoelectrical properties, low thiol passivation rate, and poor morphology with a power conversion efficiency (PCE) of approximately 11%. Although polymeric HTMs have been introduced to address these issues, maximizing efficiency and achieving green‐solvent processability and thermal stability for commercialization is necessary. Here, we synthesize a novel benzodifuran (BDF)‐based HTM (asy‐ranPBTBDF) showing an electron‐deficient state, low steric hindrance, and low planarity compared to those of a typical benzodithiophene (BDT)‐based HTM (asy‐ranPBTBDT). BDF properties lead to deep high occupied molecular orbital (HOMO) levels, close π–π stacking, excellent solubility, and amorphous properties related to efficiency, green‐solvent processability, and thermal stability. With these benefits, the asy‐ranPBTBDF‐based CQD‐SC showed enhanced open‐circuit voltage (VOC) (0.65 V) and PCE (13.29%) compared to those of the asy‐ranPBTBDT‐based device (0.63 V and 12.22%) in toxic processes with chlorobenzene. The asy‐ranPBTBDF‐based CQD‐SC showed a PCE of 12.51% in a green‐solvent process with 2‐methylanisole and improved thermal stability at 80 °C (83.8% retaining after 24 h) owing to less lateral crystallization than the asy‐ranPBTBDT‐based device (60.8% retaining after 24 h). [ABSTRACT FROM AUTHOR]

Published
2023
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