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检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

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NTIRE 2024 Challenge on Bracketing Image Restoration and Enhancement: Datasets, Methods and Results

NTIRE 2024 Challenge on Bracketing Image Restoration and Enh...

引用

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

作者： Zhilu Zhang Shuohao Zhang Renlong Wu Wangmeng Zuo Radu Timofte Xiaoxia Xing Hyunhee Park Sejun Song Changho Kim Xiangyu Kong Jinlong Wu Jianxing Zhang Jingfan Tan Zikun Liu Wenhan Luo Wenjie Lin Chengzhi Jiang Mingyan Han Zhen Liu Ting Jiang Jinting Luo Shen Cheng Linze Li Xinhan Niu Shuaicheng Liu Kexin Dai Kangzhen Yang Tao Hu Xiangyu Chen Yu Cao Qingsen Yan Yanning Zhang Genggeng Chen Yongqing Yang Wei Dong Xinwei Dai Yuanbo Zhou Xintao Qiu Hui Tang Wei Deng Qingquan Gao Tong Tong Peng Zhang Yifei Chen Wenbo Xiong Zhijun Song Pu Cheng Taolue Feng Yunqing He Daiguo Zhou Ying Huang Xiaowen Ma Peng Wu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Low-light photography presents significant challenges. Multi-image processing methods have made numerous attempts to obtain high-quality photos, yet remain unsatisfactory. Recently, bracketing image restoration and enhancement has received increased attention. By leveraging the full potential of multi-exposure images, several tasks (including denoising, deblurring, high dynamic range enhancement, and super-resolution) can be jointly addressed. This paper reviews the NTIRE 2024 challenge on bracketing image restoration and enhancement. In the challenge, participants are required to process multi-exposure RAW images to generate noise-free, blur-free, high dynamic range, and even higher-resolution RAW images. The challenge comprises two tracks. Track 1 does not incorporate the super-resolution task, whereas Track 2 does. Each track featured five teams participating in the final testing phase. The proposed methods establish new state-of-the-art performance benchmarks.

关键词： Photography computer vision Reviews conferences Superresolution Noise reduction Benchmark testing

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NTIRE 2020 Challenge on Image and Video Deblurring

NTIRE 2020 Challenge on Image and Video Deblurring

引用

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

作者： Seungjun Nah Sanghyun Son Radu Timofte Kyoung Mu Lee Yu Tseng Yu-Syuan Xu Cheng-Ming Chiang Yi-Min Tsai Stephan Brehm Sebastian Scherer Dejia Xu Yihao Chu Qingyan Sun Jiaqin Jiang Lunhao Duan Jian Yao Kuldeep Purpohit Maitreya Suin A.N. Rajagopalan Yuichi Ito P.S. Hrishikesh Densen Puthussery K.A. Akhil C.V. Jiji Guisik Kim P.L. Deepa Zhiwei Xiong Jie Huang Dong Liu Sangmin Kim Hyungjoon Nam Jisu Kim Jechang Jeong Shihua Huang Yuchen Fan Jiahui Yu Haichao Yu Thomas S. Huang Ya Zhou Xin Li Sen Liu Zhibo Chen Saikat Dutta Sourya Dipta Das Shivam Garg Daniel Sprague Bhrij Patel Thomas Huck Seoul National University NTIRE 2020 challenge organizers

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-of-the-art performance on image and video deblurring tasks.

关键词： Image restoration Image resolution Graphics processing units Neural networks Target tracking Kernel computer vision

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Style-ERD: Responsive and Coherent Online Motion Style Transfer

Style-ERD: Responsive and Coherent Online Motion Style Trans...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Tianxin Tao Xiaohang Zhan Zhongquan Chen Michiel van de Panne University of British Columbia University of California Davis

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in segments. However, for online animation applications, such as real-time avatar animation from motion capture, motions need to be processed as a stream with minimal latency. In this work, we realize a flexible, high-quality motion style transfer method for this setting. We propose a novel style transfer model, Style-ERD, to stylize motions in an online manner with an Encoder-Recurrent-Decoder structure, along with a novel discriminator that combines feature attention and temporal attention. Our method stylizes motions into multiple target styles with a unified model. Although our method targets online settings, it outperforms previous offline methods in motion realism and style expressiveness and provides significant gains in runtime efficiency.

关键词： computer vision Runtime Perturbation methods Motion segmentation Memory modules Animation Real-time systems

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

作者： Eduardo Pé rez-Pellitero Sibi Catley-Chandar Richard Shaw Aleš Leonardis Radu Timofte Zexin Zhang Cen Liu Yunbo Peng Yue Lin Gaocheng Yu Jin Zhang Zhe Ma Hongbin Wang Xiangyu Chen Xintao Wang Haiwei Wu Lin Liu Chao Dong Jiantao Zhou Qingsen Yan Song Zhang Weiye Chen Yuhang Liu Zhen Zhang Yanning Zhang Javen Qinfeng Shi Dong Gong Dan Zhu Mengdi Sun Guannan Chen Yang Hu Haowei Li Baozhu Zou Zhen Liu Wenjie Lin Ting Jiang Chengzhi Jiang Xinpeng Li Mingyan Han Haoqiang Fan Jian Sun Shuaicheng Liu Juan Marí n-Vega Michael Sloth Peter Schneider-Kamp Richard Rö ttger Chunyang Li Long Bao Gang He Ziyao Xu Li Xu Gen Zhan Ming Sun Xing Wen Junlin Li Jinjing Li Chenghua Li Ruipeng Gang Fangya Li Chenming Liu Shuang Feng Fei Lei Rui Liu Junxiang Ruan Tianhong Dai Wei Li Zhan Lu Hengyan Liu Peian Huang Guangyu Ren Yonglin Luo Chang Liu Qiang Tu Sai Ma Yizhen Cao Steven Tel Barthelemy Heyrman Dominique Ginhac Chul Lee Gahyeon Kim Seonghyun Park An Gia Vien Truong Thanh Nhat Mai Howoon Yoon Tu Vo Alexander Holston Sheir Zaheer Chan Y. Park Huawei Noah&#x2019 s Ark Laboratory ETH Z&#x00FC rich University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

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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Finetune like you pretrain: Improved finetuning of zero-shot vision models

Finetune like you pretrain: Improved finetuning of zero-shot...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Sachin Goyal Ananya Kumar Sankalp Garg Zico Kolter Aditi Raghunathan Carnegie Mellon University Stanford University Bosch Center for AI

Finetuning image-text models such as CLIP achieves state-of-the-art accuracies on a variety of benchmarks. However, recent works (Kumar et al., 2022; Wortsman et al., 2021) have shown that even subtle differences in the finetuning process can lead to surprisingly large differences in the final performance, both for in-distribution (ID) and out-of-distribution (OOD) data. In this work, we show that a natural and simple approach of mimicking contrastive pretraining consistently outperforms alternative finetuning approaches. Specifically, we cast downstream class labels as text prompts and continue optimizing the contrastive loss between image embeddings and class-descriptive prompt embeddings (contrastive finetuning). Our method consistently outperforms baselines across 7 distribution shift, 6 transfer learning, and 3 few-shot learning benchmarks. On WILDS-iWILDCam, our proposed approach FLYP outperforms the top of the leaderboard by 2.3% ID and 2.7% OOD, giving the highest reported accuracy. Averaged across 7 OOD datasets (2 WILDS and 5 ImageNet associated shifts), FLYP gives gains of 4.2% OOD over standard finetuning and outperforms current state-of-the-art (LP-FT) by more than 1 % both ID and OOD. Similarly, on 3 few-shot learning benchmarks, FLYP gives gains up to 4.6% over standard finetuning and 4.4% over the state-of-the-art. Thus we establish our proposed method of contrastive finetuning as a simple and intuitive state-of-the-art for supervised finetuning of image-text models like CLIP. Code is available at https://***/locuslab/FLYP.

关键词：

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NTIRE 2024 Dense and Non-Homogeneous Dehazing Challenge Report

NTIRE 2024 Dense and Non-Homogeneous Dehazing Challenge Repo...

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

作者： Codruta O. Ancuti Cosmin Ancuti Florin-Alexandru Vasluianu Radu Timofte Yidi Liu Xingbo Wang Yurui Zhu Gege Shi Xin Lu Xueyang Fu Zheng-Jun Zha Wei Dong Han Zhou Ruiyi Wang Xiaohong Liu Guangtao Zhai Jun Chen Wei Song Yichang Gao Jiahao Xiong Hualiang Lin Xianger Li Dong Li Mohab Kishawy Ruibin Li Seyed Amirreza Mousavi Rana Rauf Yangyi Liu Huan Liu MingSheng Tu Kele Xu JiaWen Chen Qisheng Xu Tao Sun Jin Guo Ben Shao Tianli Liu Mohao Wu Xingzhuo Yan Minghan Fu Lehan Yang Xin Lin Lu Qi Jincen Song Xiaoqian Hu Linwei Tao Hongming Chen Xiang Chen Chuanlong Xie Zhao Zhang Junhu Wang Yanyan Wei Suiyi Zhao Shengeng Tang Sampada Malagi Amogh Joshi Nikhil Akalwadi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Wenjing Jiang Jagadeesh Kalyanshetti Vijayalaxmi Ashok Aralikatti Yashaswini P Nitish Upasi Dikshit Hegde Ujwala Patil Sujata C

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study examines the results of the NTIRE 2024 Challenge on Dense and Non-Homogeneous Dehazing. Innovative methods were introduced and tested using a new image dataset named DNH-HAZE. The DNH-HAZE dataset comprises 50 pairs of authentic outdoor images showcasing dense and non-homogeneous haze alongside corresponding haze-free images of identical scenes. The haze was simulated using a professional setup designed to mirror real-world hazy conditions. The competition attracted 374 participants, with 16 teams presenting solutions for the final evaluation phase. The proposed solutions showed the leading edge of image dehazing technology.

关键词： computer vision Correlation Accuracy Image edge detection conferences computer architecture Network architecture

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NTIRE 2023 Challenge on Stereo Image Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Stereo Image Super-Resolution: Metho...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Wang, Longguang Guo, Yulan Wang, Yingqian Li, Juncheng Gu, Shuhang Timofte, Radu Cheng, Ming Ma, Haoyu Ma, Qiufang Sun, Xiaopeng Zhao, Shijie Sheng, Xuhan Ding, Yukan Sun, Ming Wen, Xing Zhang, Dafeng Li, Jia Wang, Fan Xie, Zheng He, Zongyao Qiu, Zidian Pan, Zilin Zhan, Zhihao Xian, Xingyuan Jin, Zhi Zhou, Yuanbo Deng, Wei Nie, Ruofeng Zhang, Jiajun Gao, Qinquan Tong, Tong Zhang, Kexin Zhang, Junpei Peng, Rui Ma, Yanbiao Jiao, Licheng Bai, Haoran Kong, Lingshun Pan, Jinshan Dong, Jiangxin Tang, Jinhui Cao, Pu Huang, Tianrui Yang, Lu Song, Qing Chen, Bingxin He, Chunhua Chen, Meiyun Guo, Zijie Luo, Shaojuan Cao, Chengzhi Wang, Kunyu Zhang, Fanrui Zhang, Qiang Mehta, Nancy Murala, Subrahmanyam Dudhane, Akshay Wang, Yujin Li, Lingen Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Chen, Junyang Li, Hao Shi, Yukai Yang, Zhijing Zou, Wenbin Zhang, Yunchen Jiang, Mingchao Yu, Zhongxin Tan, Ming Gao, Hongxia Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Chen, Jingxiang Yang, Bo Zhang, Xisheryl Li, Chenghua Yuan, Weijun Li, Zhan Deng, Ruting Zeng, Jintao Mahajan, Pulkit Mistry, Sahaj Chatterjee, Shreyas Jakhetiya, Vinit Subudhi, Badri Jaiswal, Sunil Zhang, Zhao Zheng, Huan Zhao, Suiyi Gao, Yangcheng Wei, Yanyan Wang, Bo Li, Gen Li, Aijin Sun, Lei Chen, Ke Tang, Congling Li, Yunzhe Chiang, Yuan-Chun Chen, Yi-Chung Huang, Zhi-Kai Yang, Hao-Hsiang Chen, I-Hsiang Kuo, Sy-Yen Wang, Yiheng Zhu, Gang Yang, Xingyi Liu, Songhua Jing, Yongcheng Hu, Xingyu Song, Jianwen Sun, Changming Sowmya, Arcot Park, Seung Ho Lei, Xiaoyan Wang, Jingchao Zhai, Chenbo Zhang, Yufei Cao, Weifeng Zhang, Wenlong Aviation University of Air Force China The Shenzhen Campus of Sun Yat-sen University Sun Yatsen University China National University of Defense Technology China Shanghai University China University of Electronic Science and Technology of China China University of Würzburg Germany ETH Zürich Switzerland ByteDance China Kuaishou Technology China Samsung Research China - Beijing China Sun Yat-sen University China Fuzhou University China Imperial Vision Technology Xidian University China Nanjing University of Science and Technology China Beijing University of Posts and Telecommunications China Guangdong University of Technology China The Hong Kong Polytechnic University Hong Kong University of Science and Technology of China China Indian Institute of Technology Ropar India MBZUAI Dubai United Arab Emirates Shanghai Artificial Intelligence Laboratory China Shanghai Jiao Tong University China Assiut University Egypt South China University of Technology China Fujian Normal University China GAC R&D Center Uppsala University Sweden Nanjing University of Information Science and Technology China Chinese Academy of Sciences Institute of Automation China Jinan University China Jiangxi Normal University China Indian Institute of Technology India K-Lens GmbH Hefei University of Technology China McMaster University Canada National Taiwan University Taiwan Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Harbin Institute of Technology China University of New South Wales Australia CSIRO Data61 Australia Seoul National University Korea Republic of Zhengzhou University of Light Industry China

ISBN: (纸本)9798350302493

This paper summarizes the 2nd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of the challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnification factor of ×4. Compared with single image SR, the major challenge of this challenge lies in how to exploit additional information in another viewpoint and how to maintain stereo consistency in the results. This challenge has 3 tracks, including one track on distortion (e.g., PSNR) and bicubic degradation, one track on perceptual quality (e.g., LPIPS) and bicubic degradation, as well as another track on real degradations. In total, 175, 93, and 103 participants were successfully registered for each track, respectively. In the test phase, 21, 17, and 12 teams successfully submitted results with PSNR (RGB) scores better than the baseline. This challenge establishes a new benchmark for stereo image SR. © 2023 ieee.

关键词： Optical resolving power

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$\text{NPBG++}$: Accelerating Neural Point-Based Graphics

$\text{NPBG++}$: Accelerating Neural Point-Based Graphics

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conference on computer vision and pattern recognition (CVPR)

作者： Ruslan Rakhimov Andrei-Timotei Ardelean Victor Lempitsky Evgeny Burnaev Skolkovo Institute of Science and Technology Russia Yandex Russia Artificial Intelligence Research Institute Russia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469470

We present a new system $(NPBG++)$ for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time. Our method efficiently lever-ages the multiview observations and the point cloud of a static scene to predict a neural descriptor for each point, improving upon the pipeline of Neural Point-Based Graph-ics [1] in several important ways. By predicting the descrip-tors with a single pass through the source images, we lift the requirement of per-scene optimization while also making the neural descriptors view-dependent and more suit-able for scenes with strong non-Lambertian effects. In our comparisons, the proposed system outperforms previous NVS approaches in terms of fitting and rendering runtimes while producing images of similar quality. Project page: https://***/npbgpp/.

关键词： Point cloud compression Graphics computer vision Runtime Fitting Pipelines Rendering (computer graphics)

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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...

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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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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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

作者： Yawei Li Yulun Zhang Radu Timofte Luc Van Gool Lei Yu Youwei Li Xinpeng Li Ting Jiang Qi Wu Mingyan Han Wenjie Lin Chengzhi Jiang Jinting Luo Haoqiang Fan Shuaicheng Liu Yucong Wang Minjie Cai Mingxi Li Yuhang Zhang Xian-Jun Fan Yankai Sheng Yanyu Mao Nihao Zhang Qian Wang Mingjun Zheng Long Sun Jinshan Pan Jiangxin Dong Jinhui Tang Zhongbao Yang Yan Wang Erlin Pan Qixuan Cai Xinan Dai Magauiya Zhussip Nikolay Kalyazin Dmitry Vyal Xueyi Zou Youliang Yan Heaseo Chung Jin Zhang Gaocheng Yu Feng Zhang Hongbin Wang Bohao Liao Zhibo Du Yu-Liang Wu Gege Shi Long Peng Yang Wang Yang Cao Zhengjun Zha Zhi-Kai Huang Yi-Chung Chen Yuan-Chun Chiang Hao-Hsiang Yang Wei-Ting Chen Hua-En Chang I-Hsiang Chen Chia-Hsuan Hsieh Sy-Yen Kuo Xin Liu Jiahao Pan Hongyuan Yu Weichen Yu Lin Ge Jiahua Dong Yajun Zou Zhuoyuan Wu Binnan Han Xiaolin Zhang Heng Zhang Xuanwu Yin Kunlong Zuo Weijian Deng Hongjie Yuan Zengtong Lu Mingyu Ouyang Wenzhuo Ma Nian Liu Hanyou Zheng Yuantong Zhang Junxi Zhang Zhenzhong Chen Garas Gendy Nabil Sabor Jingchao Hou Guanghui He Yurui Zhu Xi Wang Xueyang Fu Zheng-Jun Zha Daheng Yin Mengyang Liu Baijun Chen Ao Li Lei Luo Kangjun Jin Ce Zhu Xiaoming Zhang Chengxing Xie Linze Li Haiteng Meng Tianlin Zhang Tianrui Li Xiaole Zhao Zhao Zhang Baiang Li Huan Zheng Suiyi Zhao Yangcheng Gao Jiahuan Ren Kang Hu Jingpeng Shi Zhijian Wu Dingjiang Huang Jinchen Zhu Hui Li Qianru Xv Tianle Liu Shizhuang Weng Gang Wu Junpeng Jiang Xianming Liu Junjun Jiang Mingjian Zhang Jing Hu Chengxu Wu Qinrui Fan Chengming Feng Ziwei Luo Shu Hu Siwei Lyu Xi Wu Xin Wang Computer Vision Lab ETH Zurich

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution.

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