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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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Trident Dehazing Network

Trident Dehazing Network

引用

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

作者： Liu, Jing Wu, Haiyan Xie, Yuan Qu, Yanyun Ma, Lizhuang East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Xiamen Univ Sch Informat Sci & Engn Xiamen Fujian Peoples R China

ISBN: (纸本)9781728193601

Most existing dehazing methods are not robust to nonhomogeneous haze. Meanwhile, the information of dense haze region is usually unknown and hard to estimate, leading to blurry in dehaze result for those regions. Focusing on these two issues, we propose a novel coarse-to-fine model, namely Trident Dehazing Network (TDN), to learn the hazy to hazy-free image mapping with automatic haze density recognition. In detail, TDN is composed of three sub-nets: the Encoder-Decoder Net (EDN) is the main net of TDN to reconstruct the coarse hazy-free feature;the Detail Refinement sub-Net (DRN) helps to refine the high frequency details that was easily lost in the pooling layers in the encoder;and the Haze Density Map Generation sub-Net (HDMGN) can automatically distinguish the thick haze region with thin one, to prevent over-dehazing or under-dehazing in regions of different haze density. Moreover, we propose a frequency domain loss function to make supervision of different frequency band more uniform. Extensive experimental results on synthetic and real datasets demonstrate that our proposed TDN outperforms the state-of-the-arts with better fidelity and perceptual, generalizing well on both dense haze and nonhomogeneous haze scene. Our method won the first place in NTIRE2020 nonhomogeneous dehazing challenge.

关键词： computer vision

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Dynamic Inference: A New Approach Toward Efficient Video Action recognition

Dynamic Inference: A New Approach Toward Efficient Video Act...

引用

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

作者： Wu, Wenhao He, Dongliang Tan, Xiao Chen, Shifeng Yang, Yi Wen, Shilei Chinese Acad Sci Shenzhen Inst Adv Technol MMLab Beijing Peoples R China Baidu Inc Dept Comp Vis Technol Vis Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Technol Sydney Sydney NSW Australia Baidu Beijing Peoples R China

ISBN: (纸本)9781728193601

Though action recognition in videos has achieved great success recently, it remains a challenging task due to the massive computational cost. Designing lightweight networks is a possible solution, but it may degrade the recognition performance. In this paper, we innovatively propose a general dynamic inference idea to improve inference efficiency by leveraging the variation in the distinguishability of different videos. The dynamic inference approach can be achieved from aspects of the network depth and the number of input video frames, or even in a joint input-wise and network depth-wise manner. In a nutshell, we treat input frames and network depth of the computational graph as a 2-dimensional grid, and several checkpoints are placed on this grid in advance with a prediction module. The inference is carried out progressively on the grid by following some predefined route, whenever the inference process comes across a checkpoint, an early prediction can be made depending on whether the early stop criteria meets. For the proof-of-concept purpose, we instantiate several dynamic inference frameworks. In these instances, we overcome the drawback of limited temporal coverage resulted from an early prediction by a novel frame permutation scheme, and alleviate the conflict between progressive computation and video temporal relation modeling by introducing the online temporal shift module. Extensive experiments are conducted to thoroughly analyze the effectiveness of our ideas and to inspire future research efforts. Results on various datasets also evident the superiority of our approach.

关键词： Forecasting

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Learning Sparse Neural Networks Through Mixture-Distributed Regularization

Learning Sparse Neural Networks Through Mixture-Distributed ...

引用

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

作者： Huang, Chang-Ti Chen, Jun-Cheng Wu, Ja-Ling Natl Taiwan Univ Taipei Taiwan Acad Sinica Taipei Taiwan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

L-0-norm regularization is one of the most efficient approaches to learn a sparse neural network. Due to its discrete nature, differentiable and approximate regularizations based on the concrete distribution [31] or its variants are proposed as alternatives;however, the concrete relaxation suffers from high-variance gradient estimates and is limited to its own concrete distribution. To address these issues, in this paper, we propose a more general framework for relaxing binary gates through mixture distributions. With the proposed method, any mixture pair of distributions converging to delta(0) and delta(1) can be applied to construct smoothed binary gates. We further introduce a reparameterization method for the smoothed binary gates drawn from mixture distributions to enable efficient gradient gradient-based optimization under the proposed deep learning algorithm. Extensive experiments are conducted, and the results show that the proposed approach achieves better performance in terms of pruned architectures, structured sparsity and the reduced number of floating point operations (FLOPs) as compared with other state-of-the-art sparsity-inducing methods.

关键词： Logic gates Training Neurons computer architecture Artificial neural networks Computational modeling Exponential distribution

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Thermal Image Super-Resolution Challenge - PBVS 2020

Thermal Image Super-Resolution Challenge - PBVS 2020

引用

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

作者： Rivadeneira, Rafael E. Sappa, Angel D. Vintimilla, Boris X. Guo, Lin Hou, Jiankun Mehri, Armin Behjati Ardakani, Parichehr Patel, Heena Chudasama, Vishal Prajapati, Kalpesh Upla, Kishor P. Ramachandra, Raghavendra Raja, Kiran Busch, Christoph Almasri, Feras Debeir, Olivier Nathan, Sabari Kansal, Priya Gutierrez, Nolan Mojra, Bardia Beksi, William J. ESPOL Escuela Super Politecn Litoral CIDIS Fac Ingn Elect & Comp 30-5 Via PerimetralPOB 09-01-5863 Guayaquil Ecuador Comp Vis Ctr Campus UAB Barcelona 08193 Spain Oklahoma State Univ Stillwater OK 74078 USA SVNIT Surat India NTNU Gjovik Norway Univ Libre Bruxelles Brussels Belgium Couger Inc Tokyo Japan Univ Texas Arlington Dept Comp Sci & Engn Robot Vis Lab Arlington TX 76019 USA

ISBN: (纸本)9781728193601

This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR), which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with state-of-the-art approaches evaluated under a common framework. The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, mid-resolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 super-resolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered high-resolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.

关键词： computer vision

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FoNet: A Memory-efficient Fourier-based Orthogonal Network for Object recognition

FoNet: A Memory-efficient Fourier-based Orthogonal Network f...

引用

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

作者： Wei, Feng Uyen Trang Nguyen Jiang, Hui York Univ Dept Elect Engn & Comp Sci 4700 Keele St Toronto ON M3J 1P3 Canada

ISBN: (纸本)9781728193601

The memory consumption of most Convolutional Neural Network (CNN) architectures grows rapidly with the increasing depth of the network, which is a major constraint for efficient network training and inference on modern GPUs with limited memory. Several studies show that the feature maps (as generated after the convolutional layers) are the big bottleneck in this memory problem. Often, these feature maps mimic natural photographs in the sense that their energy is concentrated in the spectral domain. In this paper, we propose a Fourier-based Orthogonal Network (FoNet) that incorporates orthogonal representations and performs both the convolution and the activation operations in the spectral domain to achieve memory reduction. The performance of our FoNet is evaluated on four standard object recognition benchmarks (i.e., MNIST, CIFAR-10, SVHN, and ImageNet), and compared with four state-of-the-art implementations (i.e., LeNet, AlexNet, VGG, and DenseNet). Encouragingly, FoNet is able to reduce memory consumption by about 60% without significant loss of performance for all tested network architectures.

关键词： Convolution

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NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution:...

引用

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

作者： Zhang, Kai Gu, Shuhang Timofte, Radu Shang, Taizhang Dai, Qiuju Zhu, Shengchen Yang, Tong Guo, Yandong Jo, Younghyun Yang, Sejong Kim, Seon Joo Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Liu, Jing Yoon, Kwangjin Jeon, Taegyun Akita, Kazutoshi Ooba, Takeru Ukita, Norimichi Luo, Zhipeng Yao, Yuehan Xu, Zhenyu He, Dongliang Wu, Wenhao Ding, Yukang Li, Chao Li, Fu Wen, Shilei Li, Jianwei Yang, Fuzhi Yang, Huan Fu, Jianlong Kim, Byung-Hoon Baek, JaeHyun Ye, Jong Chul Fan, Yuchen Huang, Thomas S. Lee, Junyeop Lee, Bokyeung Min, Jungki Kim, Gwantae Lee, Kanghyu Park, Jaihyun Mykhailych, Mykola Zhong, Haoyu Shi, Yukai Yang, Xiaojun Yang, Zhijing Lin, Liang Zhao, Tongtong Peng, Jinjia Wang, Huibing Jin, Zhi Wu, Jiahao Chen, Yifu Shang, Chenming Zhang, Huanrong Min, Jeongki Hrishikesh, P. S. Puthussery, Densen Jiji, C., V Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland OPPO Res Dongguan Guangdong Peoples R China Yonsei Univ Seoul South Korea Facebook Menlo Pk CA USA Qingdao Hiimage Technol Co Ltd Hisense Visual Technol Co Ltd Qingdao Peoples R China Xidian Univ Xian Peoples R China East China Normal Univ ECNU Multimedia & Comp Vis Lab Shanghai Peoples R China SI Analyt Co Ltd 441 Expo Ro Daejeon 34051 South Korea Toyota Technol Inst TTI Toyota Japan DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Peking Univ Beijing Peoples R China Founder Grp State Key Lab Digital Publishing Technol Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Microsoft Res Beijing Peoples R China Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Amazon Web Serv Seattle WA USA Univ Illinois Champaign IL USA Korea Univ Seoul South Korea Wix Com Ltd Tel Aviv Israel Guangdong Univ Technol Guangzhou Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Dalian Maritime Univ Dalian Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China Coll Engn Trivandrum Trivandrum Kerala India

ISBN: (纸本)9781728193601

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.

关键词： computer vision

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Bifuse: Monocular 360◦ depth estimation via bi-projection fusion

Bifuse: Monocular 360◦ depth estimation via bi-projection f...

引用

2020 ieee/CVF conference on computer vision and pattern recognition, cvpr 2020

作者： Wang, Fu-En Yeh, Yu-Hsuan Sun, Min Chiu, Wei-Chen Tsai, Yi-Hsuan National Tsing Hua University Taiwan National Chiao Tung University Taiwan ASUS AICS Department NEC Labs America MOST Joint Research Center for AI Technology and All Vista Healthcare

Depth estimation from a monocular 360◦ image is an emerging problem that gains popularity due to the availability of consumer-level 360◦ cameras and the complete surrounding sensing capability. While the standard of 360◦ imaging is under rapid development, we propose to predict the depth map of a monocular 360◦ image by mimicking both peripheral and foveal vision of the human eye. To this end, we adopt a two-branch neural network leveraging two common projections: equirectangular and cubemap projections. In particular, equirectangular projection incorporates a complete field-of-view but introduces distortion, whereas cubemap projection avoids distortion but introduces discontinuity at the boundary of the cube. Thus we propose a bi-projection fusion scheme along with learnable masks to balance the feature map from the two projections. Moreover, for the cubemap projection, we propose a spherical padding procedure which mitigates discontinuity at the boundary of each face. We apply our method to four panorama datasets and show favorable results against the existing state-of-the-art methods. © 2020 ieee

关键词： computer vision

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MAGSAC++, a fast, reliable and accurate robust estimator

MAGSAC++, a fast, reliable and accurate robust estimator

引用

2020 ieee/CVF conference on computer vision and pattern recognition, cvpr 2020

作者： Barath, Daniel Noskova, Jana Ivashechkin, Maksym Matas, Jiri Visual Recognition Group Department of Cybernetics Czech Technical University Prague Czech Republic Machine Perception Research Laboratory MTA SZTAKI Budapest Hungary

We propose MAGSAC++ and Progressive NAPSAC sampler, P-NAPSAC in short. In MAGSAC++, we replace the model quality and polishing functions of the original method by an iteratively re-weighted least-squares fitting with weights determined via marginalizing over the noise scale. MAGSAC++ is fast - often an order of magnitude faster - and more geometrically accurate than MAGSAC. P-NAPSAC merges the advantages of local and global sampling by drawing samples from gradually growing neighborhoods. Exploiting that nearby points are more likely to originate from the same geometric model, P-NAPSAC finds local structures earlier than global samplers. We show that the progressive spatial sampling in P-NAPSAC can be integrated with PROSAC sampling, which is applied to the first, location-defining, point. The methods are tested on homography and fundamental matrix fitting on six publicly available datasets. MAGSAC combined with P-NAPSAC sampler is superior to state-of-the-art robust estimators in terms of speed, accuracy and failure rate. © 2020 ieee.

关键词： Iterative methods

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NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Metho...

引用

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

作者： Abdelhamed, Abdelrahman Afifi, Mahmoud Timofte, Radu Brown, Michael S. Cao, Yue Zhang, Zhilu Zuo, Wangmeng Zhang, Xiaoling Liu, Jiye Chen, Wendong Wen, Changyuan Liu, Meng Lv, Shuailin Zhang, Yunchao Pan, Zhihong Li, Baopu Xi, Teng Fan, Yanwen Yu, Xiyu Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Yu, Songhyun Park, Bumjun Jeong, Jechang Liu, Shuai Zong, Ziyao Nan, Nan Li, Chenghua Yang, Zengli Bao, Long Wang, Shuangquan Bai, Dongwoon Lee, Jungwon Kim, Youngjung Rho, Kyeongha Shin, Changyeop Kim, Sungho Tang, Pengliang Zhao, Yiyun Zhou, Yuqian Fan, Yuchen Huang, Thomas Li, Zhihao Shah, Nisarg A. Liu, Wei Yan, Qiong Zhao, Yuzhi Mozejko, Marcin Latkowski, Tomasz Treszczotko, Lukasz Szafraniuk, Michal Trojanowski, Krzysztof Wu, Yanhong Michelini, Pablo Navarrete Hu, Fengshuo Lu, Yunhua Kim, Sujin Kim, Wonjin Lee, Jaayeon Choi, Jang-Hwan Zhussip, Magauiya Khassenov, Azamat Kim, Jong Hyun Cho, Hwechul Kansal, Priya Nathan, Sabari Ye, Zhangyu Lu, Xiwen Wu, Yaqi Yang, Jiangxin Cao, Yanlong Tang, Siliang Cao, Yanpeng Maggioni, Matteo Marras, Ioannis Tanay, Thomas Slabaugh, Gregory Yan, Youliang Kang, Myungjoo Choi, Han-Soo Song, Kyungmin Xu, Shusong Lu, Xiaomu Wang, Tingniao Lei, Chunxia Liu, Bin Gupta, Rajat Kumar, Vineet York Univ York N Yorkshire England Swiss Fed Inst Technol Zurich Switzerland Harbin Inst Technol Harbin Peoples R China Huawei Shenzhen Peoples R China Baidu Res Seattle WA USA Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Hanyang Univ Seoul South Korea North China Univ Technol Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Samsung Semicond Inc San Jose CA USA Agcy Def Dev Seoul South Korea Beijing Univ Posts & Telecommun Beijing Peoples R China Univ Illinois Champaign IL USA Nanjing Univ Nanjing Peoples R China Indian Inst Technol Jodhpur Rajasthan India SenseTime Res Hong Kong Peoples R China TCL Res Europe Warsaw Poland BOE Artificial Intelligence & Big Data Res Inst Beijing Peoples R China Seoul Natl Univ Seoul South Korea ST Unitas Seoul South Korea Ewha Womans Univ Seoul South Korea Couger Inc Tokyo Japan Zhejiang Univ Hangzhou Peoples R China Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Harbin Inst Technol Shenzhen Shenzhen Peoples R China Huawei Technol Res & Dev UK Ltd Noahs Ark Lab London London England Dahua Technol Hangzhou Peoples R China Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9781728193601

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track similar to 250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://***/siddplus_data.

关键词： computer vision

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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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