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Thermal Image Super-Resolution Challenge - PBVS 2021

Thermal Image Super-Resolution Challenge - PBVS 2021

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rivadeneira, Rafael E. Sappa, Angel D. Vintimilla, Boris X. Nathan, Sabari Kansal, Priya Mehri, Armin Ardakani, Parichehr Behjati Dalal, Anurag Akula, Aparna Sharma, Darshika Pandey, Shashwat Kumar, Basant Yao, Jiaxin Wu, Rongyuan Feng, Kai Li, Ning Zhao, Yongqiang Patel, Heena Chudasama, Vishal Prajapati, Kalpesh Sarvaiya, Anjali Upla, Kishor P. Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Almasri, Feras Vandamme, Thomas Debeir, Olivier Gutierrez, Nolan B. Nguyen, Quan H. Beksi, William J. Escuela Super Politecn Litoral ESPOL Fac Ingn Elect & Comp CIDIS Campus Gustavo Galindo Km 30-5 Via Perimetral Guayaquil Ecuador Comp Vis Ctr Campus UAB Barcelona 08193 Spain Couger Inc Tokyo Japan Cent Sci Instruments Org Chandigarh India Indian Inst Engn Sci & Technol Sibpur India MNNIT Allahabad Prayagraj India Northwestern Polytech Univ Xian Peoples R China SVNIT Surat India NTNU Gjovik Norway Univ Libre Bruxelles Brussels Belgium Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9781665448994

This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used;only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the x2 SR obtained from the given MR thermal image;this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results out-performed those from the first challenge, thus showing an improvement in both evaluation metrics.

关键词： Training Measurement computer vision conferences Superresolution Cameras pattern recognition

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Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report

Fast and Accurate Single-Image Depth Estimation on Mobile De...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Raspberry Pi Trading Ltd Cambridge England AI Witchlabs Zurich Switzerland Tencent GY Lab Shenzhen Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan Peoples R China Chinese Acad Sci Inst Automat Nanjing Artificial Intelligence Chip Res Beijing Peoples R China Black Sesame Technol Inc Singapore Singapore Korea Adv Inst Sci & Technol Visual Media Lab Daejeon South Korea Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Natl Cheng Kung Univ Multimedia & Comp Vis Lab Tainan Taiwan Samsung Res UK Cambridge England OPPO Res Inst Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper.

关键词： Meters computer vision Image resolution conferences Estimation Real-time systems pattern recognition

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IndoFashion : Apparel Classification for Indian Ethnic Clothes

IndoFashion : Apparel Classification for Indian Ethnic Cloth...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Rajput, Pranjal Singh Aneja, Shivangi Delft Univ Technol Delft Netherlands Tech Univ Munich Munich Germany

ISBN: (纸本)9781665448994

Cloth categorization is an important research problem that is used by e-commerce websites for displaying correct products to the end-users. Indian clothes have a large number of clothing categories both for men and women. The traditional Indian clothes like "Saree" and "Dhoti" are worn very differently from western clothes like t-shirts and jeans. Moreover, the style and patterns of ethnic clothes have a very different distribution from western outfits. Thus the models trained on standard cloth datasets fail on ethnic outfits. We introduce the first large-scale ethnic dataset of over 106K images with 15 different categories for fine-grained classification of Indian ethnic clothes. We gathered a diverse dataset from a large number of Indian e-commerce websites. We then evaluate several baselines for the cloth classification task on our dataset. We obtain 88.43% classification accuracy. We hope that our dataset would foster research in the development of several algorithms such as cloth classification, landmark detection, especially for ethnic clothes. The dataset is publicly available here (1).

关键词： computer vision conferences Clothing pattern recognition Classification algorithms Task analysis Standards

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NTIRE 2021 Learning the Super-Resolution Space Challenge

NTIRE 2021 Learning the Super-Resolution Space Challenge

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Busch, Christoph Chen, Yang Cheng, Jian Chudasama, Vishal Gang, Ruipeng Gao, Shangqi Gao, Kun Gong, Laiyun Han, Qingrui Huang, Chao Jin, Zhi Jo, Younghyun Kim, Seon Joo Kim, Younggeun Lee, Seungjun Lei, Yuchen Li, Chu-Tak Li, Chenghua Li, Ke Liu, Zhi-Song Liu, Youming Nan, Nan Park, Seung-Ho Patel, Heena Peng, Shichong Prajapati, Kalpesh Qi, Haoran Raja, Kiran Ramachandra, Raghavendra Siu, Wan-Chi Son, Donghee Song, Ruixia Upla, Kishor Wang, Li-Wen Wang, Yatian Wang, Junwei Wu, Qianyu Xu, Xinhua Yang, Sejong Yuan, Zhen Zhang, Liting Zhang, Huanrong Zhang, Junkai Zhang, Yifan Zhang, Zhenzhou Zhou, Hangqi Zhu, Aichun Zhuang, Xiahai Zou, Jiaxin Swiss Fed Inst Technol Zurich Switzerland Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Zhejiang Univ Hangzhou Peoples R China NetEaseYunXin Beijing Peoples R China Yonsei Univ Seoul South Korea Seoul Natl Univ Seoul South Korea Univ Ulsan Coll Med Asan Med Ctr Ulsan South Korea Lomin Inc Portland OR USA Fudan Univ Shanghai Peoples R China Simon Fraser Univ Burnaby BC Canada Caritas Inst Higher Educ Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China North China Univ Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China CASIA Beijing Peoples R China NRTA Beijing Peoples R China NCUT Beijing Peoples R China Nanjing Tech Univ Sch Comp Sci & Technol Nanjing Peoples R China Southeast Univ Lab Image Sci & Technol Nanjing Peoples R China SVNIT Surat Gujarat India NTNU Trondheim Norway

ISBN: (纸本)9781665448994

This paper reviews the NTIRE 2021 challenge on learning the super-Resolution space. It focuses on the participating methods and final results. The challenge addresses the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image. The model must thus be capable of sampling diverse outputs, rather than just generating a single SR image. The goal of the challenge is to spur research into developing learning formulations and models better suited for the highly ill-posed SR problem. And thereby advance the state-of-the-art in the broader SR field. In order to evaluate the quality of the predicted SR space, we propose a new evaluation metric and perform a comprehensive analysis of the participating methods. The challenge contains two tracks: 4x and 8 x scale factor. In total, 11 teams competed in the final testing phase.

关键词： Measurement computer vision conferences Superresolution Predictive models pattern recognition

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Vln↻bert: A Recurrent vision-and-Language BERT for Navigation

Vln↻bert: A Recurrent Vision-and-Language BERT for Navigati...

引用

2021 ieee/CVF conference on computer vision and pattern recognition, cvpr 2021

作者： Hong, Yicong Wu, Qi Qi, Yuankai Rodriguez-Opazo, Cristian Gould, Stephen The Australian National University Australian Centre for Robotic Vision Australia The University of Adelaide Australian Centre for Robotic Vision Australia

Accuracy of many visiolinguistic tasks has benefited significantly from the application of vision-and-language (V&L) BERT. However, its application for the task of vision- and-language navigation (VLN) remains limited. One reason for this is the difficulty adapting the BERT architecture to the partially observable Markov decision process present in VLN, requiring history-dependent attention and decision making. In this paper we propose a recurrent BERT model that is time-aware for use in VLN. Specifically, we equip the BERT model with a recurrent function that maintains cross-modal state information for the agent. Through extensive experiments on R2R and REVERIE we demonstrate that our model can replace more complex encoder-decoder models to achieve state-of-the-art results. Moreover, our approach can be generalised to other transformer-based architectures, supports pre-training, and is capable of solving navigation and referring expression tasks simultaneously. © 2021 ieee

关键词： Adaptation models Visualization Technological innovation Navigation Bit error rate computer architecture Markov processes

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Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results

Wild Face Anti-Spoofing Challenge 2023: Benchmark and Result...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Dong Wang Jia Guo Qiqi Shao Haochi He Zhian Chen Chuanbao Xiao Ajian Liu Sergio Escalera Hugo Jair Escalante Zhen Lei Jun Wan Jiankang Deng MoreDian InsightFace CASIA Computer Vision Center (UAB) University of Barcelona INAOE

Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. Despite substantial advancements, the generalization of existing approaches to real-world applications remains challenging. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets, which often leads to overfitting during training or saturation during testing. In terms of quantity, the number of spoof subjects is a critical determinant. Most datasets comprise fewer than 2,000 subjects. With regard to diversity, the majority of datasets consist of spoof samples collected in controlled environments using repetitive, mechanical processes. This data collection methodology results in homogenized samples and a dearth of scenario diversity. To address these shortcomings, we introduce the Wild Face Anti-Spoofing (WFAS) dataset, a large-scale, diverse FAS dataset collected in unconstrained settings. Our dataset encompasses 853,729 images of 321,751 spoof subjects and 529,571 images of 148,169 live subjects, representing a substantial increase in quantity. Moreover, our dataset incorporates spoof data obtained from the internet, spanning a wide array of scenarios and various commercial sensors, including 17 presentation attacks (PAs) that encompass both 2D and 3D forms. This novel data collection strategy markedly enhances FAS data diversity. Leveraging the WFAS dataset and Protocol 1 (Known-Type), we host the Wild Face Anti-Spoofing Challenge at the cvpr2023 workshop. Additionally, we meticulously evaluate representative methods using Protocol 1 and Protocol 2 (Unknown-Type). Through an in-depth examination of the challenge outcomes and benchmark baselines, we provide insightful analyses and propose potential avenues for future research. The dataset is released under Insightface 1 .

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NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessm...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Xin Li Kun Yuan Yajing Pei Yiting Lu Ming Sun Chao Zhou Zhibo Chen Radu Timofte Wei Sun Haoning Wu Zicheng Zhang Jun Jia Zhichao Zhang Linhan Cao Qiubo Chen Xiongkuo Min Weisi Lin Guangtao Zhai JianHui Sun Tianyi Wang Lei Li Han Kong Wenxuan Wang Bing Li Cheng Luo Haiqiang Wang Xiangguang Chen Wenhui Meng Xiang Pan Huiying Shi Han Zhu Xiaozhong Xu Lei Sun Zhenzhong Chen Shan Liu Fangyuan Kong Haotian Fan Yifang Xu Haoran Xu Mengduo Yang Jie Zhou Jiaze Li Shijie Wen Mai Xu Da Li Shunyu Yao Jiazhi Du Wangmeng Zuo Zhibo Li Shuai He Anlong Ming Huiyuan Fu Huadong Ma Yong Wu Fie Xue Guozhi Zhao Lina Du Jie Guo Yu Zhang Huimin Zheng Junhao Chen Yue Liu Dulan Zhou Kele Xu Qisheng Xu Tao Sun Zhixiang Ding Yuhang Hu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://***/lixinustc/KVQ-Challenge-cvpr-NTIRE2024.

关键词： Training computer vision Reviews Databases conferences Benchmark testing Quality assessment

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EfficientNet-SAM: A Novel EffecientNet with Spatial Attention Mechanism for COVID-19 Detection in Pulmonary CT Scans

EfficientNet-SAM: A Novel EffecientNet with Spatial Attentio...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Ramy Farag Parth Upadhay Jacket Demby’s Yixiang Gao Katherin Garces Montoya Seyed Mohamad Ali Tousi Gbenga Omotara Guilherme DeSouza Department of Electrical Engineering and Computer Science Vision-Guided and Intelligent Robotics Lab - ViGIR Lab University of Missouri-Columbia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning-based pipeline. Our motivation was sparked by the cvpr Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates one of the architectures in the EfficientNet "family", but with an added Spatial Attention Mechanism: EfficientNet-SAM. Also, unlike the traditional/past pipelines, which relied on a preprocessing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different models nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline outperformed all approaches presented by other teams in last year’s instance of the same challenge using the validation subset. It also placed 5th in this year’s competition, ranking less than 1.3% below the 1st place and close to 3.5% above the 6th place based on the macro-F1 score.

关键词： COVID-19 Training Visualization Attention mechanisms Computed tomography conferences Pipelines

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NTIRE 2021 Depth Guided Image Relighting Challenge

NTIRE 2021 Depth Guided Image Relighting Challenge

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： El Helou, Majed Zhou, Ruofan Susstrunk, Sabine Timofte, Radu Suin, Maitreya Rajagopalan, A. N. Wang, Yuanzhi Lu, Tao Zhang, Yanduo Wu, Yuntao Yang, Hao-Hsiang Chen, Wei-Ting Kuo, Sy-Yen Luo, Hao-Lun Zhang, Zhiguang Luo, Zhipeng He, Jianye Zhu, Zuo-Liang Li, Zhen Qiu, Jia-Xiong Kuang, Zeng-Sheng Lu, Cheng-Ze Cheng, Ming-Ming Shao, Xiu-Li Li, Chenghua Ding, Bosong Qian, Wanli Li, Fangya Li, Fu Deng, Ruifeng Lin, Tianwei Liu, Songhua Li, Xin He, Dongliang Yazdani, Amirsaeed Guo, Tiantong Monga, Vishal Nsampi, Ntumba Elie Hu, Zhongyun Wang, Qing Nathan, Sabari Kansal, Priya Zhao, Tongtong Zhao, Shanshan Ecole Polytech Fed Lausanne Lausanne Switzerland Swiss Fed Inst Technol Zurich Switzerland ASUSTeK Comp Inc ASUS Intelligent Cloud Serv Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Chinese Acad Sci Inst Automat Beijing Peoples R China Couger Inc Tokyo Japan DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Penn State Univ Sch Elect Engn & Comp Sci Informat Proc & Algorithms Lab iPAL University Pk PA 16802 USA Indian Inst Technol Madras Chennai Tamil Nadu India Nankai Univ Tianjin Peoples R China Northwestern Polytech Univ Sch Comp Sci Comp Vis & Computat Photog Grp Xian Peoples R China Dalian Maritime Univ Dalian Peoples R China China Everbright Bank Shanghai Peoples R China Baidu Inc Dept Comp Vis Vis Beijing Peoples R China Wuhan Inst Technol Hubei Key Lab Intelligent Robot Wuhan Peoples R China

ISBN: (纸本)9781665448994

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In this paper, we review the NTIRE 2021 depth guided image relighting challenge. We rely on the VIDIT dataset for each of our two challenge tracks, including depth information. The first track is on one-to-one relighting where the goal is to transform the illumination setup of an input image (color temperature and light source position) to the target illumination setup. In the second track, the any-to-any relighting challenge, the objective is to transform the illumination settings of the input image to match those of another guide image, similar to style transfer. In both tracks, participants were given depth information about the captured scenes. We had nearly 250 registered participants, leading to 18 confirmed team submissions in the final competition stage. The competitions, methods, and final results are presented in this paper.

关键词： Geometry computer vision Target tracking Image color analysis conferences Lighting Transforms

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NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

NTIRE 2021 Challenge on Quality Enhancement of Compressed Vi...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Ren Timofte, Radu Liu, Jing Xu, Yi Zhang, Xinjian Zhao, Minyi Zhou, Shuigeng Chan, Kelvin C. K. Zhou, Shangchen Xu, Xiangyu Loy, Chen Change Li, Xin Liu, Fanglong Zheng, He Jiang, Lielin Zhang, Qi He, Dongliang Li, Fu Dang, Qingqing Huang, Yibin Maggioni, Matteo Fu, Zhongqian Xiao, Shuai Li, Cheng Tanay, Thomas Song, Fenglong Chao, Wentao Guo, Qiang Liu, Yan Li, Jiang Qu, Xiaochao Hou, Dewang Yang, Jiayu Jiang, Lyn You, Di Zhang, Zhenyu Mou, Chong Koshelev, Iaroslav Ostyakov, Pavel Somov, Andrey Hao, Jia Zou, Xueyi Zhao, Shijie Sun, Xiaopeng Liao, Yiting Zhang, Yuanzhi Wang, Qing Zhan, Gen Guo, Mengxi Li, Junlin Lu, Ming Ma, Zhan Michelini, Pablo Navarrete Wang, Hai Chen, Yiyun Guo, Jingyu Zhang, Liliang Yang, Wenming Kim, Sijung Oh, Syehoon Wang, Yucong Cai, Minjie Hao, Wei Shi, Kangdi Li, Liangyan Chen, Jun Gao, Wei Liu, Wang Zhang, Xiaoyu Zhou, Linjie Lin, Sixin Wang, Ru Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Bilibili Inc Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Huawei Technol Co Ltd Huawei Noahs Ark Lab Shenzhen Peoples R China Meitu Inc MTLab Beijing Peoples R China Peking Univ Shenzhen Peoples R China Tencent Shenzhen Peoples R China Skolkovo Inst Sci & Technol Moscow Russia Huawei Noahs Ark Lab Shenzhen Peoples R China HiSilicon Shanghai Technol CO LTD Shanghai Peoples R China ByteDance Ltd Shenzhen Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China BOE Technol Grp Co Ltd Beijing Peoples R China Tsinghua Univ Shenzhen Peoples R China SZ Da Jiang Innovat Sci & Technol Co Ltd Shenzhen Peoples R China Bluedot Seoul South Korea Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China McMaster Univ Hamilton ON Canada Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with focus on proposed solutions and results. In this challenge, the new Large-scale Diverse Video (LDV) dataset is employed. The challenge has three tracks. Tracks 1 and 2 aim at enhancing the videos compressed by HEVC at a fixed QP, while Track 3 is designed for enhancing the videos compressed by x265 at a fixed bit-rate. Besides, the quality enhancement of Tracks 1 and 3 targets at improving the fidelity (PSNR), and Track 2 targets at enhancing the perceptual quality. The three tracks totally attract 482 registrations. In the test phase, 12 teams, 8 teams and 11 teams submitted the final results of Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of video quality enhancement. The homepage of the challenge: https://***/RenYang-home/NTIRE21_VEnh

关键词： computer vision Target tracking conferences Quality assessment pattern recognition Video recording

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