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

作者： Brian Moser Federico Raue Jö rn Hees Andreas Dengel German Research Center for Artificial Intelligence (DFKI) Germany TU Kaiserslautern Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Neural Architecture Search (NAS) defines the design of Neural Networks as a search problem. Unfortunately, NAS is computationally intensive because of various possibilities depending on the number of elements in the design and the possible connections between them. In this work, we extensively analyze the role of the dataset size based on several sampling approaches for reducing the dataset size (unsupervised and supervised cases) as an agnostic approach to reduce search time. We compared these techniques with four common NAS approaches in NAS-Bench201 in roughly 1,400 experiments on CIFAR-100. One of our surprising findings is that in most cases we can reduce the amount of training data to 25%, consequently also reducing search time to 25%, while at the same time maintaining the same accuracy as if training on the full dataset. In addition, some designs derived from subsets out-perform designs derived from the full dataset by up to 22 p.p. accuracy.

关键词： Training computer vision conferences Neural networks Training data computer architecture Search problems

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Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey

Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS...

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

作者： Marcos V. Conde Zhijun Lei Wen Li Ioannis Katsavounidis Radu Timofte Min Yan Xin Liu Qian Wang Xiaoqian Ye Zhan Du Tiansen Zhang Zhiyuan Li Hao Wei Chenyang Ge Jiangtao Lv Long Sun Jinshan Pan Jiangxin Dong Jinhui Tang Menghan Zhou Yiqiang Yan Kihwan Yoon Ganzorig Gankhuyag Jae-Hyeon Lee Ui-Jin Choi Hyeon-Cheol Moon Tae-Hyun Jeong Yoonmo Yang Jae-Gon Kim Jinwoo Jeong Sunjei Kim Xintao Qiu Yuanbo Zhou Kongxian Wu Xinwei Dai Hui Tang Wei Deng Qingquan Gao Tong Tong Long Peng Jiaming Guo Xin Di Bohao Liao Zhibo Du Peize Xia Renjing Pei Yang Wang Yang Cao Zhengjun Zha Bingnan Han Hongyuan Yu Zhuoyuan Wu Cheng Wan Yuqing Liu Haodong Yu Jizhe Li Zhijuan Huang Yuan Huang Yajun Zou Xianyu Guan Qi Jia Heng Zhang Xuanwu Yin Kunlong Zuo Dongyang Zhang Tianle Liu Huaian Chen Yi Jin CAIDAS & IFI Computer Vision Lab. University of Wurzbürg Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a novel benchmark for efficient image upscaling as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to up scale compressed images from 540p to native 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a diverse test set containing diverse 4K images ranging from digital art to gaming and photography. The images are compressed using the modern AVIF codec, instead of JPEG. All the proposed methods improve PSNR fidelity over Lanczos interpolation, and process images under 30ms. Out of the 160 participants, 25 teams submitted their code. This survey considers only the most novel solutions models, making it the most comprehensive benchmark on real-time SR of compressed images using modern codecs.

关键词： Surveys Photography Image coding Codecs Superresolution Transform coding Benchmark testing

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Towards efficient feature sharing in MIMO architectures

Towards efficient feature sharing in MIMO architectures

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

作者： Ré my Sun Alexandre Ramé Clé ment Masson Nicolas Thome Matthieu Cord MLIAI SIR Sorbonne Universit&#x00E9Thales Land and Air Systems France MLIAI SIR Sorbonne Universit&#x00E9 Thales Land and Air Systems France Vertigo CEDRIC Conservatoire National des Arts et M&#x00E9 tiers MLIAI SIR Sorbonne Universit&#x00E9Valeo.ai

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful in their use of parameters. Indeed, we highlight in this paper that the learned subnetwork fail to share even generic features which limits their applicability on smaller mobile and AR/VR devices. We posit this behavior stems from an ill-posed part of the multi-input multi-output framework. To solve this issue, we propose a novel unmixing step in MIMO architectures that allows subnetworks to properly share features. Preliminary experiments on CIFAR 100 show our adjustments allow feature sharing and improve model performance for small architectures.

关键词： Performance evaluation computer vision Impedance matching conferences Neural networks computer architecture pattern recognition

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NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods...

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

作者： Xiaoning Liu Zongwei Wu Ao Li Florin-Alexandru Vasluianu Yulun Zhang Shuhang Gu Le Zhang Ce Zhu Radu Timofte Zhi Jin Hongjun Wu Chenxi Wang Haitao Ling Yuanhao Cai Hao Bian Yuxin Zheng Jing Lin Alan Yuille Ben Shao Jin Guo Tianli Liu Mohao Wu Yixu Feng Shuo Hou Haotian Lin Yu Zhu Peng Wu Wei Dong Jinqiu Sun Yanning Zhang Qingsen Yan Wenbin Zou Weipeng Yang Yunxiang Li Qiaomu Wei Tian Ye Sixiang Chen Zhao Zhang Suiyi Zhao Bo Wang Yan Luo Zhichao Zuo Mingshen Wang Junhu Wang Yanyan Wei Xiaopeng Sun Yu Gao Jiancheng Huang Hongming Chen Xiang Chen Hui Tang Yuanbin Chen Yuanbo Zhou Xinwei Dai Xintao Qiu Wei Deng Qinquan Gao Tong Tong Mingjia Li Jin Hu Xinyu He Xiaojie Guo Sabarinathan Sabarinathan K Uma A Sasithradevi B Sathya Bama S. Mohamed Mansoor Roomi V. Srivatsav Jinjuan Wang Long Sun Qiuying Chen Jiahong Shao Yizhi Zhang Marcos V. Conde Daniel Feijoo Juan C. Benito Alvaro García Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy Ali Murtaza Uswah Khairuddin Ahmad ’Athif Mohd Faudzi Sampada Malagi Amogh Joshi Nikhil Akalwadi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Wenyi Lian Wenjing Lian Jagadeesh Kalyanshetti Vijayalaxmi Ashok Aralikatti Palani Yashaswini Nitish Upasi Dikshit Hegde Ujwala Patil Sujata C Xingzhuo Yan Wei Hao Minghan Fu Pooja Choksy Anjali Sarvaiya Kishor Upla Kiran Raja Hailong Yan Yunkai Zhang Baiang Li Jingyi Zhang Huan Zheng

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.

关键词： computer vision Image resolution Reviews conferences Lighting pattern recognition Image enhancement

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Is synthetic voice detection research going into the right direction?

Is synthetic voice detection research going into the right d...

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

作者： Stefano Borzì Oliver Giudice Filippo Stanco Dario Allegra Department of Mathematics and Computer Science University of Catania Italy IT Dept Banca d&#x2019 Italia Applied Research Team Rome Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Machine Learning, and in general Artificial Intelligence approaches, brought a great advance in each and every field of computer Science increasing accuracy levels of predictors in any known problem. Indeed, this evolution enabled the construction of effective frameworks and solutions able to be used in investigative and forensics scenarios for detection of fakes and, in general, manipulations in multimedia contents. On the other hand, can we trust these systems? Is research activity going in the right direction? Are we just taking the low-hanging fruit without taking into account many real-case-in-the-wild situations? The purpose of this paper is to raise an alert to the research community in the specific context of synthetic voice detection, where data available for training is not big enough to give sufficient trust in the techniques available in the literature. To this aim, an exploratory investigation of the most common voice spoofing dataset was carried out and it was surprisingly easy to build simple classifiers without any Deep Learning techniques. Simple considerations on bitrate were sufficient to achieve an effective detection performance.

关键词： Deep learning Training computer vision Image color analysis Forensics conferences Bit rate

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ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture recognition

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ieee TRANSACTIONS ON CYBERNETICS 2022年第5期52卷 3422-3433页

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Univ Southern Calif Dept Comp Sci Los Angeles CA 90089 USA JD Finance Mountain View CA 94043 USA Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Xian Key Lab Big Data & Intelligent Vis Xian 710071 Peoples R China Univ Barcelona Comp Vis Ctr Barcelona 08007 Spain Univ Tartu Inst Technol iCV Lab EE-50090 Tartu Estonia PwC Finland Helsinki 00180 Finland Hasan Kalyoncu Univ Fac Engn TR-27100 Gaziantep Turkey ChaLearn San Francisco CA 94115 USA Univ Paris Saclay F-91190 St Aubin France Baidu Res Inst Deep Learning Beijing 100193 Peoples R China Natl Engn Lab Deep Learning Technol & Applicat Beijing 100193 Peoples R China Westlake Univ Hangzhou 310024 Peoples R China Macau Univ Sci & Technol Taipa Macau Peoples R China

The ChaLearn large-scale gesture recognition challenge has run twice in two workshops in conjunction with the International conference on pattern recognition (ICPR) 2016 and International conference on computer vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. It describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. In this article, we discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition and provide a detailed analysis of the current methods for large-scale isolated and continuous gesture recognition. In addition to the recognition rate and mean Jaccard index (MJI) as evaluation metrics used in previous challenges, we introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) method, determining video division points based on skeleton points. Experiments show that the proposed Bi-LSTM outperforms state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.

关键词： Gesture recognition Measurement Task analysis Training conferences computer vision Bidirectional long short-term memory (Bi-LSTM) gesture recognition RGB-D

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Thermal Image Super-Resolution Challenge Results - PBVS 2022

Thermal Image Super-Resolution Challenge Results - PBVS 2022

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

作者： Rafael E. Rivadeneira Angel D. Sappa Boris X. Vintimilla Jin Kim Dogun Kim Zhihao Li Yingchun Jian Bo Yan Leilei Cao Fengliang Qi Hongbin Wang Rongyuan Wu Lingchen Sun Yongqiang Zhao Lin Li Kai Wang Yicheng Wang Xuanming Zhang Huiyuan Wei Chonghua Lv Qigong Sun Xiaolin Tian Zhuang Jia Jiakui Hu Chenyang Wang Zhiwei Zhong Xianming Liu Junjun Jiang Escuela Superior Polit&#x00E9 cnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic.

关键词： Training Deep learning conferences Superresolution Transformers Particle measurements pattern recognition

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CoRe: Color Regression for Multicolor Fashion Garments

CoRe: Color Regression for Multicolor Fashion Garments

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

作者： Alexandre Ramé Arthur Douillard Charles Ollion Sorbonne Universit&#x00E9Heuritech Heuritech

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Developing deep networks that analyze fashion garments has many real-world applications. Among all fashion attributes, color is one of the most important yet challenging to detect. Existing approaches are classification-based and thus cannot go beyond the list of discrete predefined color names. In this paper, we handle color detection as a regression problem to predict the exact RGB values. That’s why in addition to a first color classifier, we include a second regression stage for refinement in our newly proposed architecture. This second step combines two attention models: the first depends on the type of clothing, the second depends on the color previously detected by the classifier. Our final prediction is the weighted spatial pooling over the image pixels RGB values, where the illumination has been corrected. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment. In our experiments, we show the benefits of each component of our architecture.

关键词： Visualization computer vision Image color analysis conferences Clothing Lighting computer architecture

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NTIRE 2024 Image Shadow Removal Challenge Report

NTIRE 2024 Image Shadow Removal Challenge Report

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

作者： Florin-Alexandru Vasluianu Tim Seizinger Zhuyun Zhou Zongwei Wu Cailian Chen Radu Timofte Wei Dong Han Zhou Yuqiong Tian Jun Chen Xueyang Fu Xin Lu Yurui Zhu Xi Wang Dong Li Jie Xiao Yunpeng Zhang Zheng-Jun Zha Zhao Zhang Suiyi Zhao Bo Wang Yan Luo Yanyan Wei Zhihao Zhao Long Sun Tingting Yang Jinshan Pan Jiangxin Dong Jinhui Tang Bilel Benjdira Mohammed Nassif Anis Koubaa Ahmed Elhayek Anas M. Ali Kyotaro Tokoro Kento Kawai Kaname Yokoyama Takuya Seno Yuki Kondo Norimichi Ukita Chenghua Li Bo Yang Zhiqi Wu Gao Chen Yihan Yu Sixiang Chen Kai Zhang Tian Ye Wenbin Zou Yunlong Lin Zhaohu Xing Jinbin Bai Wenhao Chai Lei Zhu Ritik Maheshwari Rakshank Verma Rahul Tekchandani Praful Hambarde Satya Narayan Tazi Santosh Kumar Vipparthi Subrahmanyam Murala Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy Fan Gao Weidan Yan Wenze Shao Dengyin Zhang Bin Chen Siqi Zhang Yanxin Qian Yuanbin Chen Yuanbo Zhou Tong Tong Rongfeng Wei Ruiqi Sun Yue Liu Nikhil Akalwadi Amogh Joshi Sampada Malagi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Ali Murtaza Uswah Khairuddin Ahmad ’Athif Mohd Faudzi Adinath Dukre Vivek Deshmukh Shruti S. Phutke Ashutosh Kulkarni Anil Gonde Arun karthik K Manasa N Shri Hari Priya Wei Hao Xingzhuo Yan Minghan Fu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building on the last year edition, the current challenge was organized in two tracks, with a track focused on increased fidelity reconstruction, and a separate ranking for high performing perceptual quality solutions. Track 1 (fidelity) had 214 registered participants, with 17 teams submitting in the final phase, while Track 2 (perceptual) registered 185 participants, resulting in 18 final phase submissions. Both tracks were based on data from the WSRD dataset, simulating interactions between self-shadows and cast shadows, with a large variety of represented objects, textures, and materials. Improved image alignment enabled increased fidelity reconstruction, with restored frames mostly indistinguishable from the references images for top performing solutions.

关键词： computer vision Reviews Image synthesis conferences Buildings Image restoration pattern recognition

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Analysis and Extensions of Adversarial Training for Video Classification

Analysis and Extensions of Adversarial Training for Video Cl...

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

作者： Kaleab A. Kinfu René Vidal Mathematical Institute for Data Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Adversarial training (AT) is a simple yet effective defense against adversarial attacks to image classification systems, which is based on augmenting the training set with attacks that maximize the loss. However, the effectiveness of AT as a defense for video classification has not been thoroughly studied. Our first contribution is to show that generating optimal attacks for video requires carefully tuning the attack parameters, especially the step size. Notably, we show that the optimal step size varies linearly with the attack budget. Our second contribution is to show that using a smaller (sub-optimal) attack budget at training time leads to a more robust performance at test time. Based on these findings, we propose three defenses against attacks with variable attack budgets. The first one, Adaptive AT, is a technique where the attack budget is drawn from a distribution that is adapted as training iterations proceed. The second, Curriculum AT, is a technique where the attack budget is increased as training iterations proceed. The third, Generative AT, further couples AT with a denoising generative adversarial network to boost robust performance. Experiments on the UCF101 dataset demonstrate that the proposed methods improve adversarial robustness against multiple attack types.

关键词： Training computer vision conferences Noise reduction Generative adversarial networks Robustness pattern recognition

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