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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2024 Challenge on HR Depth from Images of Specular and...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Pierluigi Zama Ramirez Fabio Tosi Luigi Di Stefano Radu Timofte Alex Costanzino Matteo Poggi Samuele Salti Stefano Mattoccia Yangyang Zhang Cailin Wu Zhuangda He Shuangshuang Yin Jiaxu Dong Yangchenxu Liu Hao Jiang Jun Shi Yong A Yixiang Jin Dingzhe Li Bingxin Ke Anton Obukhov Tinafu Wang Nando Metzger Shengyu Huang Konrad Schindler Yachuan Huang Jiaqi Li Junrui Zhang Yiran Wang Zihao Huang Tianqi Liu Zhiguo Cao Pengzhi Li Jui-Lin Wang Wenjie Zhu Hui Geng Yuxin Zhang Long Lan Kele Xu Tao Sun Qisheng Xu Sourav Saini Aashray Gupta Sahaj K. Mistry Aryan Shukla Vinit Jakhetiya Sunil Jaiswal Yuejin Sun Zhuofan Zheng Yi Ning Jen-Hao Cheng Hou-I Liu Hsiang-Wei Huang Cheng-Yen Yang Zhongyu Jiang Yi-Hao Peng Aishi Huang Jenq-Neng Hwang

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2024. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The challenge proposes two tracks on stereo and single-image depth estimation, attracting about 120 registered participants. In the final testing stage, 2 and 8 participating teams submitted their models and fact sheets for the two tracks.

关键词： computer vision conferences Computational modeling Estimation Market research pattern recognition Image restoration

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MvAV-pix2pixHD: Multi-view Aerial View Image Translation

MvAV-pix2pixHD: Multi-view Aerial View Image Translation

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jun Yu Keda Lu Shenshen Du Lin Xu Peng Chang Houde Liu Bin Lan Tianyu Liu University of Science and Technology of China Ping An Technology Co. Ltd China PAII Inc. Tsinghua Shenzhen International Graduate School Jianghuai Advance Technology Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-modal aerial view image translation involves converting aerial images from one modality to another while preserving basic details and features. These modalities encompass Synthetic Aperture Radar (SAR), Infrared (IR), Visible Light (RGB), Electro-Optical (EO), and other image types. Recently, various methods have been proposed to tackle this task, but the focus tends to be on paired image research, overlooking the discrepancies found in aerial images of the same location captured at different times and angles, termed incomplete matching or multi-view image translation. Consequently, we propose MvAV-pix2pixHD to address this issue. For multi-view data sampling, we propose two methods: random sampling and time-priority sampling. Additionally, within the pix2pixHD framework, we introduce an inverse generator to ensure the basic semantic features of the generated images and incorporate three robust loss functions to constrain the authenticity of the generated images. We conduct extensive experiments on two multi-view image translation tasks in the Multi-modal Aerial View Imagery Challenge: Translation (MAVIC-T). Experimental results demonstrate the superiority of our proposed method, and we achieved second place in the MAVIC-T competition in the 20th IEEE workshop on Perception Beyond the Visible Spectrum of the CVPR 2024.

关键词： computer vision Image resolution conferences Computational modeling Semantics computer architecture Generators

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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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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 2022 Challenge on High Dynamic Range Imaging: Methods and Results

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Perez-Pellitero, Eduardo Catley-Chandar, Sibi Shaw, Richard Leonardis, Ales Timofte, Radu Zhang, Zexin Liu, Cen Peng, Yunbo Lin, Yue Yu, Gaocheng Zhang, Jin Ma, Zhe Wang, Hongbin Chen, Xiangyu Wang, Xintao Wu, Haiwei Liu, Lin Dong, Chao Zhou, Jiantao Yan, Qingsen Zhang, Song Chen, Weiye Liu, Yuhang Zhang, Zhen Zhang, Yanning Shi, Javen Qinfeng Gong, Dong Zhu, Dan Sun, Mengdi Chen, Guannan Hu, Yang Li, Haowei Zou, Baozhu Liu, Zhen Lin, Wenjie Jiang, Ting Jiang, Chengzhi Li, Xinpeng Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Marin-Vega, Juan Sloth, Michael Schneider-Kamp, Peter Rottger, Richard Li, Chunyang Bao, Long He, Gang Xu, Ziyao Xu, Li Zhan, Gen Sun, Ming Wen, Xing Li, Junlin Li, Jinjing Li, Chenghua Gang, Ruipeng Li, Fangya Liu, Chenming Feng, Shuang Lei, Fei Liu, Rui Ruan, Junxiang Dai, Tianhong Li, Wei Lu, Zhan Liu, Hengyan Huang, Peian Ren, Guangyu Luo, Yonglin Liu, Chang Tu, Qiang Ma, Sai Cao, Yizhen Tel, Steven Heyrman, Barthelemy Ginhac, Dominique Lee, Chul Kim, Gahyeon Park, Seonghyun An Gia Vien Truong Thanh Nhat Mai Yoon, Howoon Tu Vo Holston, Alexander Zaheer, Sheir Park, Chan Y. Huawei Noahs Ark Lab Hong Kong Peoples R China Univ Wurzburg Wurzburg Germany Swiss Fed Inst Technol Zurich Switzerland Netease Games AI Lab Shanghai Peoples R China AntGroup Hangzhou Peoples R China Univ Macau Macau Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Tencent PCG Shenzhen Peoples R China Univ Sci & Technol China Hefei Peoples R China Univ Adelaide Adelaide SA Australia Xidian Univ Xian Peoples R China Northwestern Polytech Univ Xian Peoples R China Univ New South Wales Sydney NSW Australia BOE Technol Grp Co Ltd Beijing Peoples R China CZUR Technol Grp Co Ltd Liaoning Peoples R China Megvii Technol Beijing Peoples R China Univ Southern Denmark Dept Math & Comp Sci IMADA Aarhus Denmark Esoft Syst Odense Denmark Xiaomi Beijing Peoples R China ByteDance Beijing Peoples R China Kuaishou Technol Beijing Peoples R China Commun Univ China Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China NRTA Acad Broadcasting Sci Beijing Peoples R China Tetras AI Technol Hangzhou Peoples R China Imperial Coll London London England Tsinghua Univ Beijing Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Sun Yat Sen Univ Guangzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Beijing Inst Technol Beijing Peoples R China NRTA Acad Broadcasting Sciencience Beijing Peoples R China Univ Burgundy ImViA Lab Dijon France Dongguk Univ Dept Multimedia Engn Seoul South Korea Gachon Univ Seongnam South Korea KC Machine Learning Lab Seoul South Korea

ISBN: (纸本)9781665487399

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under- or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).

关键词： computer vision Runtime conferences Imaging Dynamic range Particle measurements Market research

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NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Liu, Huan Li, Liangyan Wu, Zijun Dong, Yubo Li, Yuyan Qiu, Tian He, Yu Lu, Yonghong Wu, Yinwei Jiang, Zhenxiang Liu, Songhua Yang, Xingyi Jing, Yongcheng Benjdira, Bilel Ali, Anas M. Koubaa, Anis Yang, Hao-Hsiang Chen, I-Hsiang Chen, Wei-Ting Huang, Zhi-Kai Chen, Yi-Chung Hsieh, Chia-Hsuan Chang, Hua-En Chiang, Yuan-Chun Kuo, Sy-Yen Guo, Yu Gao, Yuan Liu, Ryan Wen Lu, Yuxu Qu, Jingxiang He, Shengfeng Ren, Wenqi Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Yang, Lehan Wu, Alex Jiahao Mai, Tiancheng Cong, Xiaofeng Yin, Xuemeng Yin, Xuefei Emad, Hazim Abdallah, Ahmed Yasser, Yahya Elshahat, Dalia Elbaz, Esraa Li, Zhan Kuang, Wenqing Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhang, Zhao Wei, Yanyan Wang, Junhu Zhao, Suiyi Zheng, Huan Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Jiang, Kui Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph ETcTI Universitatea Politehnica Timisoara Romania ICTEAM UCL Belgium Computer Vision Lab University of Wuerzburg Germany Computer Vision Lab ETH Zurich Switzerland Department of Electrical and Computer Engineering McMaster University Canada Department of Electrical and Computer Engineering University of Alberta Canada McMaster University Canada Xidian University China Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Wuhan University of Technology China Singapore Management University Singapore Singapore Sun Yat-sen University China Electrical Engineering Department Pennsylvania State University United States The University of Sydney Australia Southeast University China University of California Los Angeles United States Beijing Jiaotong University China Mansoura Univeristy Egypt College of Information Science and Technology Jinan University China Department of Information Technology Uppsala University Sweden Hefei University of Technology China Zhejiang Dahua Technology China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams submitted solutions for the final testing phase. The proposed solutions demonstrated the cutting-edge in image dehazing technology. © 2023 IEEE.

关键词： Demulsification

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NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset,...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Perez-Pellitero, Eduardo Catley-Chandar, Sibi Leonardis, Ales Timofte, Radu Wang, Xian Li, Yong Wang, Tao Song, Fenglong Liu, Zhen Lin, Wenjie Li, Xinpeng Rao, Qing Jiang, Ting Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Chen, Xiangyu Liu, Yihao Zhang, Zhengwen Qiao, Yu Dong, Chao Chee, Evelyn Yi Lyn Shen, Shanlan Duan, Yubo Chen, Guannan Sun, Mengdi Gao, Yan Zhang, Lijie Akhil, K. A. Jiji, C., V Sharif, S. M. A. Naqvi, Rizwan Ali Biswas, Mithun Kim, Sungjun Xia, Chenjie Zhao, Bowen Ye, Zhangyu Lu, Xiwen Cao, Yanpeng Yang, Jiangxin Cao, Yanlong Rosh, Green K. S. Lomte, Sachin Deepak Krishnan, Nikhil Prasad, B. H. Pawan Huawei Noahs Ark Lab Shenzhen Peoples R China Megvii Technol Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Black Sesame Technol Singapore Singapore BOE Technol Co Ltd Beijing Peoples R China Coll Engn Trivandrum Trivandrum Kerala India Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China Zhejiang Univ Key Lab Adv Mfg Technol Hangzhou Peoples R China Sejong Univ Seoul South Korea Rigel IT Dhaka Bangladesh Samsung R&D Inst India Bangalore SRI B Bangalore Karnataka India

ISBN: (纸本)9781665448994

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021. This manuscript focuses on the newly introduced dataset, the proposed methods and their results. The challenge aims at estimating a HDR image from one or multiple respective low-dynamic range (LDR) observations, which might suffer from underor over-exposed regions and different sources of noise. The challenge is composed by two tracks: In Track 1 only a single LDR image is provided as input, whereas in Track 2 three differently-exposed LDR images with inter-frame motion are available. In both tracks, the ultimate goal is to achieve the best objective HDR reconstruction in terms of PSNR with respect to a ground-truth image, evaluated both directly and with a canonical tonemapping operation.

关键词： computer vision Tracking conferences Imaging Dynamic range Market research pattern recognition

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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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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

NTIRE 2024 Quality Assessment of AI-Generated Content Challe...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiaohong Liu Xiongkuo Min Guangtao Zhai Chunyi Li Tengchuan Kou Wei Sun Haoning Wu Yixuan Gao Yuqin Cao Zicheng Zhang Xiele Wu Radu Timofte Fei Peng Huiyuan Fu Anlong Ming Chuanming Wang Huadong Ma Shuai He Zifei Dou Shu Chen Huacong Zhang Haiyi Xie Chengwei Wang Baoying Chen Jishen Zeng Jianquan Yang Weigang Wang Xi Fang Xiaoxin Lv Jun Yan Tianwu Zhi Yabin Zhang Yaohui Li Yang Li Jingwen Xu Jianzhao Liu Yiting Liao Junlin Li Zihao Yu Fengbin Guan Yiting Lu Xin Li Hossein Motamednia S. Farhad Hosseini-Benvidi Ahmad Mahmoudi-Aznaveh Azadeh Mansouri Ganzorig Gankhuyag Kihwan Yoon Yifang Xu Haotian Fan Fangyuan Kong Shiling Zhao Weifeng Dong Haibing Yin Li Zhu Zhiling Wang Bingchen Huang Avinab Saha Sandeep Mishra Shashank Gupta Rajesh Sureddi Oindrila Saha Luigi Celona Simone Bianco Paolo Napoletano Raimondo Schettini Junfeng Yang Jing Fu Wei Zhang Wenzhi Cao Limei Liu Han Peng Weijun Yuan Zhan Li Yihang Cheng Yifan Deng Haohui Li Bowen Qu Yao Li Shuqing Luo Shunzhou Wang Wei Gao Zihao Lu Marcos V. Conde Xinrui Wang Zhibo Chen Ruling Liao Yan Ye Qiulin Wang Bing Li Zhaokun Zhou Miao Geng Rui Chen Xin Tao Xiaoyu Liang Shangkun Sun Xingyuan Ma Jiaze Li Mengduo Yang Haoran Xu Jie Zhou Shiding Zhu Bohan Yu Pengfei Chen Xinrui Xu Jiabin Shen Zhichao Duan Erfan Asadi Jiahe Liu Qi Yan Youran Qu Xiaohui Zeng Lele Wang Renjie Liao

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact *** video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

关键词： Image quality computer vision conferences Computational modeling Market research Quality assessment pattern recognition

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