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NTIRE 2022 Spectral Recovery Challenge and Data Set

NTIRE 2022 Spectral Recovery Challenge and Data Set

引用

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

作者： Arad, Boaz Timofte, Radu Yahel, Rony Morag, Nimrod Bernat, Amir Cai, Yuanhao Lin, Jing Lin, Zudi Wang, Haoqian Zhang, Yulun Pfister, Hanspeter Van Gool, Luc Liu, Shuai Li, Yongqiang Feng, Chaoyu Lei, Lei Li, Jiaojiao Du, Songcheng Wu, Chaoxiong Leng, Yihong Song, Rui Zhang, Mingwei Song, Chongxing Zhao, Shuyi Lang, Zhiqiang Wei, Wei Zhang, Lei Dian, Renwei Shan, Tianci Guo, Anjing Feng, Chengguo Liu, Jinyang Agarla, Mirko Bianco, Simone Buzzelli, Marco Celona, Luigi Schettini, Raimondo He, Jiang Xiao, Yi Xiao, Jiajun Yuan, Qiangqiang Li, Jie Zhang, Liangpei Kwon, Taesung Ryu, Dohoon Bae, Hyokyoung Yang, Hao-Hsiang Chang, Hua-En Huang, Zhi-Kai Chen, Wei-Ting Kuo, Sy-Yen Chen, Junyu Li, Haiwei Liu, Song Uma, Sabarinathan K. Bama, B. Sathya Roomi, S. Mohamed Mansoor Oddity Tech Ltd New York NY USA Voyage81 Ltd New York NY USA Swiss Fed Inst Technol Computat Vis Lab Zurich Switzerland Univ Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Acad Coll Tel Aviv Yaffo Tel Aviv Israel Tel Aviv Univ Tel Aviv Israel Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Harvard Univ Visual Comp Grp Cambridge MA USA JMU Wurzburg Ctr Artificial Intelligence & Data Sci Wurzburg Germany Xiaomi Inc Beijing Peoples R China Xidian Univ Xian Peoples R China Northwestern Polytech Univ Xian Peoples R China Northwestern Polytech Univ Changan Campus Xian Peoples R China Hunan Univ Changsha Hunan Peoples R China Univ Milano Bicocca Dept Informat Syst & Commun Milan Italy Wuhan Univ Sch Geodesy & Geomat Wuhan Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China Korea Adv Inst Sci & Technol Dept Bio & Brain Engn Daejeon South Korea Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Elect Engn Taipei Taiwan Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Peoples R China Couger Inc Tokyo Japan Thiagarajar Coll Engn Kamarajar Salai India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the "ARAD 1K" data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyperspectral information from synthetically generated JPEG-compressed RGB images simulating capture by a known calibrated camera, operating under partially known parameters, in a setting which includes acquisition noise. The challenge was attended by 241 teams, with 60 teams competing in the final testing phase, 12 of which provided detailed descriptions of their methodology which are included in this report. The performance of these submissions is reviewed and provided here as a gauge for the current state-of-the-art in spectral reconstruction from natural RGB images.

关键词： Training computer vision Costs conferences Neural networks Cameras pattern recognition

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On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders

On Disentanglement and Mutual Information in Semi-Supervised...

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

作者： Gordon Rodriguez, Elliott Columbia Univ Dept Stat New York NY 10027 USA

ISBN: (纸本)9781665448994

In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.

关键词： computer vision conferences pattern recognition Mutual information

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I Find Your Lack of Uncertainty in computer vision Disturbing

I Find Your Lack of Uncertainty in Computer Vision Disturbin...

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

作者： Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence Robert Hooke Str 1 D-28359 Bremen Germany

ISBN: (纸本)9781665448994

Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.

关键词： computer vision Uncertainty Computational modeling conferences Neural networks pattern recognition

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GazeCaps: Gaze Estimation with Self-Attention-Routed Capsules

GazeCaps: Gaze Estimation with Self-Attention-Routed Capsule...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wang, Hengfei Oh, Jun O Jin Chang, Hyung Na, Jin Hee Tae, Minwoo Zhang, Zhongqun Choi, Sang-Il University of Birmingham United Kingdom Dankook University Korea Republic of VTouch Inc

ISBN: (纸本)9798350302493

Gaze estimation is the task of estimating eye gaze from facial features. People tend to infer gaze by considering different facial properties from the whole image and their relations. However, existing methods rarely consider these various properties. In this paper, we propose a novel GazeCaps framework that represents various facial properties as different capsules. The capsules respond sensitively to transforms of facial properties by vectorial expression, which is effective for gaze estimation in which many facial components are nonlinearly transformed according to the direction of the head in addition to the perspective. Furthermore, we propose a Self-Attention Routing (SAR) module which can dynamically allocate attention to different capsules that contain important information and can be optimized as a single process without iterations. Through rigorous experiments, we confirm that the proposed method achieves state-of-the-art performance on various benchmarks. We also detail the generalization performance of the proposed model through a cross-dataset evaluation. © 2023 ieee.

关键词： computer vision

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Model-Based Testing computer Games: Does It Work? 18

Model-Based Testing Computer Games: Does It Work?

引用

18th ieee International conference on Software Testing, Verification and Validation workshops, ICSTW 2025

作者： Prasetya, I.S.W.B. Utrecht University Netherlands

ISBN: (纸本)9798331534677

Model-based testing (MBT) allows a target software to be tested systematically and automatically by making use of a model of the software under test. It has been successfully applied in various domains. However its application for testing computer games has not been much studied. The highly dynamic nature of computer games makes it challenging for modeling. In this paper we propose a predicate-based modeling approach coupled with an on-line test generation approach. Both aspects ease the details that need to be incorporated in the model to facilitate effective test generation. Additionally we also leverage the use of intelligent agents, so that dealing with hazards and obstacles can optionally be delegated to such an agent, hence keeping the model clean from such aspects. A case study with a game called MiniDungeon is included to discuss the viability and benefit of the approach, e.g. in terms of code coverage and ability to cover deep states. © 2025 ieee.

关键词： Automatic test pattern generation

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Efficient CNN Architecture for Multi-modal Aerial View Object Classification

Efficient CNN Architecture for Multi-modal Aerial View Objec...

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

作者： Miron, Casian Pasarica, Alexandru Timofte, Radu Gheorghe Asachi Tech Univ MCC Resources SRL Iasi Romania

ISBN: (纸本)9781665448994

The NTIRE 2021 workshop features a Multi-modal Aerial View Object Classification Challenge. Its focus is on multi-sensor imagery classification in order to improve the performance of automatic target recognition (ATR) systems. In this paper we describe our entry in this challenge, a method focused on efficiency and low computational time, while maintaining a high level of accuracy. The method is a convolutional neural network with 11 convolutions, 1 max pooling layers and 3 residual blocks which has a total of 373.130 parameters. The method ranks 3rd in the Track 2 (SAR+EO) of the challenge.

关键词： Training computer vision Target recognition conferences Systems architecture Graphics processing units computer architecture

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NTIRE 2023 Challenge on Night Photography Rendering

NTIRE 2023 Challenge on Night Photography Rendering

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Shutova, Alina Ershov, Egor Perevozchikov, Georgy Ermakov, Ivan Banić, Nikola Timofte, Radu Collins, Richard Efimova, Maria Terekhin, Arseniy Zini, Simone Rota, Claudio Buzzelli, Marco Bianco, Simone Schettini, Raimondo Lei, Chunxia Wang, Tingniao Wang, Song Liu, Shuai Feng, Chaoyu Shao, Guangqi Wang, Hao Wang, Xiaotao Lei, Lei Xu, Lu Zhang, Chao Wang, Yasi Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Kinli, Furkan Özcan, Bariş Kiraç, Furkan Chung, Hyerin Lee, Nakyung Kwak, Sung Keun Conde, Marcos Elezabi, Omar Seizinger, Tim Vasluianu, Florin Hsieh, Chia-Hsuan Chen, Wei-Ting Yang, Hao-Hsiang Huang, Zhi-Kai Chang, Hua-En Chen, I-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Kuo, Sy-Yen Russia Gideon Brothers Croatia Swiss Federal Institute of Technology in Zurich Switzerland University of Milano - Bicocca Italy Zhejiang Dahua Technology China Xiaomi Inc. China Beijing Samsung Teleconmmunication R&D Center China Vision and Graphics Lab Özyeǧin University Turkey Fishency Innovation Norway T-Fashion Inc. Turkey CJ OliveNetworks AI Research Korea Republic of University of Wuerzburg Computer Vision Lab CAIDAS Germany ServiceNow United States Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan University of Würzburg Germany

ISBN: (纸本)9798350302493

This paper presents a review of the NTIRE 2023 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, participants were not provided with a large training dataset for the target sensor. Instead, this time they were given images of a color checker illuminated by a known light source. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. The highest ranking solutions were further ranked by Richard Collins, a renowned photographer. The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://***/ © 2023 ieee.

关键词： Photography

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Tilted Cross-Entropy (TCE): Promoting Fairness in Semantic Segmentation

Tilted Cross-Entropy (TCE): Promoting Fairness in Semantic S...

引用

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

作者： Szabo, Attila Jamali-Rad, Hadi Mannava, Siva-Datta Shell Global Solut Int BV Amsterdam Netherlands Delft Univ Technol TU Delft Delft Netherlands

ISBN: (纸本)9781665448994

Traditional empirical risk minimization (ERM) for semantic segmentation can disproportionately advantage or disadvantage certain target classes in favor of an (unfair but) improved overall performance. Inspired by the recently introduced tilted ERM (TERM), we propose tilted cross-entropy (TCE) loss and adapt it to the semantic segmentation setting to minimize performance disparity among target classes and promote fairness. Through quantitative and qualitative performance analyses, we demonstrate that the proposed Stochastic TCE for semantic segmentation can offer improved overall fairness by efficiently minimizing the performance disparity among the target classes of Cityscapes.

关键词： computer vision conferences Semantics Performance analysis pattern recognition Risk management

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Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN

Focus on Content not Noise: Improving Image Generation for N...

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ieee/CVF International conference on computer vision (ICCV)

作者： Utz, Jonas Weise, Tobias Schlereth, Maja Wagner, Fabian Thies, Mareike Gu, Mingxuan Uderhardt, Stefan Breininger, Katharina Friedrich Alexander Univ Erlangen Nuremberg Dept Artificial Intelligence Biomed Engn Erlangen Germany FAU Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany FAU Erlangen Nurnberg Dept Med Rheumatol & Immunol 3 Erlangen Germany FAU Erlangen Nurnberg Deutsch Zentrum Immuntherapie Erlangen Germany FAU Erlangen Nurnberg Exploratory Res Unit Opt Imaging Ctr Erlangen Erlangen Germany

ISBN: (纸本)9798350307443

Annotating nuclei in microscopy images for the training of neural networks is a laborious task that requires expert knowledge and suffers from inter- and intra-rater variability, especially in fluorescence microscopy. Generative networks such as CycleGAN can inverse the process and generate synthetic microscopy images for a given mask, thereby building a synthetic dataset. However, past works report content inconsistencies between the mask and generated image, partially due to CycleGAN minimizing its loss by hiding shortcut information for the image reconstruction in high frequencies rather than encoding the desired image content and learning the target task. In this work, we propose to remove the hidden shortcut information, called steganography, from generated images by employing a low pass filtering based on the discrete cosine transform (DCT). We show that this increases coherence between generated images and cycled masks and evaluate synthetic datasets on a downstream nuclei segmentation task. Here we achieve an improvement of 5.4 percentage points in the F1-score compared to a vanilla CycleGAN. Integrating advanced regularization techniques into the CycleGAN architecture may help mitigate steganography-related issues and produce more accurate synthetic datasets for nuclei segmentation.

关键词： cycle gan data generation dct fluorescence microscopy nuclei segmentation steganography synthetic data

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NTIRE 2023 Challenge on Image Denoising: Methods and Results

NTIRE 2023 Challenge on Image Denoising: Methods and Results

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Van Gool, Luc Timofte, Radu Tu, Zhijun Du, Kunpeng Wang, Hailing Chen, Hanting Li, Wei Wang, Xiaofei Hu, Jie Wang, Yunhe Kong, Xiangyu Wu, Jinlong Zhang, Dafeng Zhang, Jianxing Liu, Shuai Bai, Furui Feng, Chaoyu Wang, Hao Zhang, Yuqian Shao, Guangqi Wang, Xiaotao Lei, Lei Xu, Rongjian Zhang, Zhilu Chen, Yunjin Ren, Dongwei Zuo, Wangmeng Wu, Qi Han, Mingyan Cheng, Shen Li, Haipeng Jiang, Ting Jiang, Chengzhi Li, Xinpeng Luo, Jinting Lin, Wenjie Yu, Lei Fan, Haoqiang Liu, Shuaicheng Arora, Aditya Zamir, Syed Waqas Vazquez-Corral, Javier Derpanis, Konstantinos G. Brown, Michael S. Li, Hao Zhao, Zhihao Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Bo Chen, Jingxiang Li, Chenghua Zhang, Xi Zhang, Zhao Ren, Jiahuan Ji, Zhicheng Miao, Kang Zhao, Suiyi Zheng, Huan Wei, YanYan Liu, Kangliang Du, Xiangcheng Liu, Sijie Zheng, Yingbin Wu, Xingjiao Jin, Cheng Wang, Yan Chen, Jiayin Wu, Xiaoxuan Chen, Huiming Zheng, Xing Chen, Yejia Irny, Rajeev Koundinya, Sriharsha Kamath, Vighnesh Khandelwal, Gaurav Khowaja, Sunder Ali Yoon, Jiseok Lee, Ik Hyun Chen, Shijie Zhao, Chengqiang Yang, Huabin Zhang, Zhongjian Huang, Junjia Zhang, Yanru Computer Vision Lab D-ITET Eth Zürich Switzerland Computer Vision Lab Ifi & Caidas University of Würzburg Germany Huawei Noah'Ark Lab Canada Huawei Consumer Business Group China Xiaomi Inc. China Harbin Institute of Technology China Megvii Technology China York University Toronto Canada Abu Dhabi United Arab Emirates Computer Vision Center Universitat Autonòma de Barcelona Spain Nanjing University of Science and Technology China Nanjing University of Information Science and Technology China China Fudan University China Videt Technology South China University of Technology China India University of Sindh Pakistan Iklab Inc. Korea Republic of Iklab Inc. Tech University of Korea Korea Republic of Xuzhou Medical University China Southwest Jiaotong University China

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising. © 2023 ieee.

关键词： Image denoising

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