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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024"

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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and ...

引用

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

作者： Yuekun Dai Dafeng Zhang Xiaoming Li Zongsheng Yue Chongyi Li Shangchen Zhou Ruicheng Feng Peiqing Yang Zhezhu Jin Guanqun Liu Chen Change Loy S-Lab Nanyang Technological University Samsung Research China Nankai University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024.

关键词： Photography Image sensors Industries computer vision Reviews conferences Focusing

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HirFormer: Dynamic High Resolution Transformer for Large-Scale Image Shadow Removal

HirFormer: Dynamic High Resolution Transformer for Large-Sca...

引用

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

作者： Xin Lu Yurui Zhu Xi Wang Dong Li Jie Xiao Yunpeng Zhang Xueyang Fu Zheng-Jun Zha University of Science and Technology of China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing image restoration models have limited performance in high-resolution image shadow removal tasks, particularly in handling complex background information and unevenly distributed shadows. To address this challenge, we propose a novel two-stage approach called HirFormer for high-resolution image shadow removal. The first stage, Dynamic High Resolution Transformer, reconstructs the high-resolution background information and removes a significant portion of the shadows based on the Transformer architecture. The second stage, Large-scale Image Refinement, incorporates the NAFNet model to further eliminate residual shadows and address block artifacts introduced by the first stage. Experimental results on official datasets validate the superiority of our method compared to existing approaches, and our approach emerged as the winner in the fidelity track of the NTIRE 2024 Shadow Removal Challenge during the final testing competition (1st place).

关键词： Measurement computer vision Image resolution conferences computer architecture Transformers Image restoration

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Unified Face Attack Detection with Micro Disturbance and a Two-Stage Training Strategy

Unified Face Attack Detection with Micro Disturbance and a T...

引用

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

作者： Jiaruo Yu Dagong Lu Xingyue Shi Chenfan Qu Fengjun Guo IntSig Information Co. Ltd Shanghai China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face recognition systems are widely used in real-world scenarios but are susceptible to physical and digital attacks. Effective methods for unified detection of both physical face attacks and digital face attacks are essential to ensure the reliability of face recognition systems. However, how to obtain a unified face attack detection model that has adequate ability of fine-grained perception and cross-domain generalization ability remains an open challenge. To address this issue, we first propose a two-stage training strategy, which utilizes unlabeled face images with masked image modeling and unleashes the potential of vision transformers. Furthermore, we propose a novel method termed as Micro Disturbance, which successfully enriches the representation distribution of forged faces and increases the diversity of the training data, thereby addressing the issue of cross-domain generalization. Attribute to the effectiveness of our proposed methods, our model finally wins the third place in the 5th Face Anti-Spoofing Challenge@CVPR2024, with an impressive ACER score of 5.511.

关键词： Training computer vision Social networking (online) Face recognition Forensics conferences Training data

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Towards Quantitative Evaluation Metrics for Image Editing Approaches

Towards Quantitative Evaluation Metrics for Image Editing Ap...

引用

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

作者： Dana Cohen Hochberg Oron Anschel Alon Shoshan Igor Kviatkovsky Manoj Aggarwal Gérard Medioni Amazon

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the rapidly evolving field of Generative AI, this work takes initial steps towards establishing a systematic approach for comparing image editing methods. Currently, there is a lack of quantitative metrics for evaluating image editing tasks, with new methods being evaluated mostly qualitatively. Our methodology involves three key components: 1) The creation of a large synthetic dataset using GAN-Control, which enables the generation of ground-truth images for consistent edits across different facial identities; 2) A matching procedure that pairs the edited images with their corresponding ground-truth; and 3) Application of the Perceptual Distance metric to matched pairs. We assessed the effectiveness of our proposed framework through a user study and a set of simulation experiments. Our results indicate that our approach can rank image-editing methods in a way that aligns with human judgment. This research seeks to lay the foundation for a comprehensive evaluation framework for image editing techniques in subsequent studies, initiating a dialogue on this topic.

关键词： Measurement computer vision Systematics Generative AI conferences pattern recognition Synthetic data

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NTIRE 2023 Video Colorization Challenge

NTIRE 2023 Video Colorization Challenge

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

作者： Kang, Xiaoyang Lin, Xianhui Zhang, Kai Hui, Zheng Xiang, Wangmeng He, Jun-Yan Li, Xiaoming Ren, Peiran Xie, Xuansong Timofte, Radu Pan, Jinshan Peng, Zhongzheng Zhang, Qiyan Dong, Jiangxin Tang, Jinhui Li, Jinjing Lin, Chichen Li, Qipei Liang, Qirong Gang, Ruipeng Liu, Xiaofeng Feng, Shuang Liu, Shuai Wang, Hao Feng, Chaoyu Bai, Furui Zhang, Yuqian Shao, Guangqi Wang, Xiaotao Lei, Lei Chen, Siqi Zhang, Yu Xu, Hanning Liu, Zheyuan Zhang, Zhao Luo, Yan Zuo, Zhichao Damo Academy Alibaba Group China Computer Vision Lab Eth Zürich Switzerland Nanyang Technological University Singapore University of Würzburg Germany Nanjing University of Science and Technology China Communication University of China Beijing100024 China Academy of Broadcasting Science Nrta Beijing100866 China Uhdtv Research and Application Laboratory Beijing100176 China Xiaomi Inc. China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Zjut China China

ISBN: (纸本)9798350302493

This paper reviews the video colorization challenge on the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2023. The target of this challenge is converting grayscale videos into color videos with better colorization performance and temporal consistency. The challenge consists of two tracks. For Track 1, the goal is achieving the best FID (Fréchet Inception Distance) while being constrained to maintain or improve over the baseline method in terms of the temporal-consistency metric. The Color Distribution Consistency (CDC) index is used as the temporal consistency evaluation metric in this challenge. For Track 2, the target is to obtain a solution with the best CDC result while being constrained to maintain or improve over the baseline method in terms of FID. We use DeOldify-video as the baseline method for two tracks. For the final testing phase of both tracks, six teams submitted fact sheets and executable code of their solutions. This report brings together descriptions and discussions of all these solutions. Both tracks use the same data and the datasets are available at this url. © 2023 ieee.

关键词： Image enhancement

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Context-aware Video Anomaly Detection in Long-Term Datasets

Context-aware Video Anomaly Detection in Long-Term Datasets

引用

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

作者： Zhengye Yang Richard J. Radke Department of ECSE Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video anomaly detection research is generally evaluated on short, isolated benchmark videos only a few minutes long. However, in real-world environments, security cameras observe the same scene for months or years at a time, and the notion of anomalous behavior critically depends on context, such as the time of day, day of week, or schedule of events. Here, we propose a context-aware video anomaly detection algorithm, Trinity, specifically targeted to these scenarios. Trinity is especially well-suited to crowded scenes in which individuals cannot be easily tracked, and anomalies are due to speed, direction, or absence of group motion. Trinity is a contrastive learning framework that aims to learn alignments between context, appearance, and motion, and uses alignment quality to classify videos as normal or anomalous. We evaluate our algorithm on both conventional benchmarks and a public webcam-based dataset we collected that spans more than three months of activity.

关键词： Schedules computer vision Target tracking conferences Contrastive learning Benchmark testing pattern recognition

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NTIRE 2022 Challenge on Night Photography Rendering

NTIRE 2022 Challenge on Night Photography Rendering

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ershov, Egor Savchik, Alex Shepelev, Denis Banic, Nikola Brown, Michael S. Timofte, Radu Koscevic, Karlo Freeman, Michael Tesalin, Vasily Bocharov, Dmitry Semenkov, Illya Subasic, Marko Loncaric, Sven Terekhin, Arseniy Liu, Shuai Feng, Chaoyu Wang, Hao Zhu, Ran Li, Yongqiang Lei, Lei Li, Zhihao Yi, Si Han, Ling-Hao Wu, Ruiqi Jin, Xin Guo, Chunle Kinli, Furkan Mentes, Sami Ozcan, Baris Kirac, Furkan Zini, Simone Rota, Claudio Buzzelli, Marco Bianco, Simone Schettini, Raimondo Li, Wei Ma, Yipeng Wang, Tao Xu, Ruikang Song, Fenglong Chen, Wei-Ting Yang, Hao-Hsiang Huang, Zhi-Kai Chang, Hua-En Kuo, Sy-Yen Liang, Zhexin Zhou, Shangchen Feng, Ruicheng Li, Chongyi Chen, Xiangyu Song, Binbin Zhang, Shile Liu, Lin Wang, Zhendong Ryu, Dohoon Bae, Hyokyoung Kwon, Taesung Desai, Chaitra Akalwadi, Nikhil Joshi, Amogh Mandi, Chinmayee Malagi, Sampada Uppin, Akash Reddy, Sai Sudheer Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma Kharkevich Inst Inst Informat Transmiss Problems Moscow Russia Gideon Bros Osijek Croatia York Univ Toronto ON Canada Swiss Fed Inst Technol Zurich Zurich Switzerland Univ Wurzburg Wurzburg Germany Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Michael Freeman Photog London England Xiaomi Inc Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Nankai Univ Tianjin Peoples R China Wuhan Univ Technol Wuhan Peoples R China Ozyegin Univ Istanbul Turkey Univ Milano Bicocca Milan Italy Huawei Noahs Ark Lab Montreal PQ Canada Natl Taiwan Univ Grad Inst Elect Engn New Taipei Taiwan Natl Taiwan Univ Dept Elect Engn New Taipei Taiwan Nanyang Technol Univ Singapore Singapore Univ Macau Taipa Macao Peoples R China Univ Sci & Technol China Hefei Peoples R China Korea Adv Inst Sci & Technol Daejeon South Korea KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://***/

关键词： Photography Visualization computer vision conferences Rendering (computer graphics) Cameras pattern recognition

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Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learning

Unveiling the Anomalies in an Ever-Changing World: A Benchma...

引用

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

作者： Nikola Bugarin Jovana Bugaric Manuel Barusco Davide Dalle Pezze Gian Antonio Susto University of Padova

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly Detection is a relevant problem in numerous real-world applications, especially when dealing with images. However, little attention has been paid to the issue of changes over time in the input data distribution, which may cause a significant decrease in performance. In this study, we investigate the problem of Pixel-Level Anomaly Detection in the Continual Learning setting, where new data arrives over time and the goal is to perform well on new and old data. We implement several state-of-the-art techniques to solve the Anomaly Detection problem in the classic setting and adapt them to work in the Continual Learning setting. To validate the approaches, we use a real-world dataset of images with pixel-based anomalies to provide a reliable benchmark and serve as a foundation for further advancements in the field. We provide a comprehensive analysis, discussing which Anomaly Detection methods and which families of approaches seem more suitable for the Continual Learning setting.

关键词： Continuing education computer vision conferences Benchmark testing pattern recognition Reliability Anomaly detection

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Generative Dataset Distillation: Balancing Global Structure and Local Details

Generative Dataset Distillation: Balancing Global Structure ...

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

作者： Longzhen Li Guang Li Ren Togo Keisuke Maeda Takahiro Ogawa Miki Haseyama Hokkaido University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a generative model. Dataset distillation has been proposed to reduce the size of the required dataset when training models. The conventional dataset distillation methods face the problem of long redeployment time and poor cross-architecture performance. Moreover, previous methods focused too much on the high-level semantic attributes between the synthetic dataset and the original dataset while ignoring the local features such as texture and shape. Based on the above understanding, we propose a new method for distilling the original image dataset into a generative model. Our method involves using a conditional generative adversarial network to generate the distilled dataset. Subsequently, we ensure balancing global structure and local details in the distillation process, continuously optimizing the generator for more information-dense dataset generation.

关键词： Training computer vision Shape Face recognition conferences Computational modeling Semantics

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Collaborative Blind Image Deblurring

Collaborative Blind Image Deblurring

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

作者： Thomas Eboli Jean-Michel Morel Gabriele Facciolo CNRS ENS Paris-Saclay Centre Borelli Université Paris-Saclay City University of Hong Kong

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Blurry images usually exhibit similar blur at various locations across the image domain, a property barely captured in nowadays blind deblurring neural networks. We show that when extracting patches of similar underlying blur is possible, jointly processing the stack of patches yields superior accuracy than handling them separately. Our collaborative scheme is implemented in a neural architecture with a pooling layer on the stack dimension. We present three practical patch extraction strategies for image sharpening, camera shake removal and optical aberration correction, and validate the proposed approach on both synthetic and real-world benchmarks. For each blur instance, the proposed collaborative strategy yields significant quantitative and qualitative improvements.

关键词： Optical design conferences Neural networks Collaboration computer architecture Optical imaging Cameras

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