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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

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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...

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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 ...

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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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Implementation of an Anomalous Human Activity recognition System

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SN computer Science 2020年第3期1卷 1-10页

作者： Shreyas, D.G. Raksha, S. Prasad, B.G. B. M. S. College of Engineering Bengaluru India

This paper brings to light one of the most prominent applications of human activity recognition which is the anomaly detection. Providing security to an individual is a major concern of any society today due to the constantly increasing actions causing threats, starting from deliberate violence to an injury caused by an accident. Mere installation of a traditional closed-circuit television is not enough as it requires a human being to constantly remain alert and monitor the cameras, which is quite inefficient. This calls for the need to develop an automated security system that identifies abnormal activities in real time and brings immediate help to the victims. But it is time-consuming to recognize activities from lengthy surveillance videos and hence a new implementation idea which adaptively compresses the videos before passing it through the activity recognition system has been proposed. Adaptive video compression is a unique compression technology that compresses only the least significant parts of the video, retaining the objects of interest. By combining adaptive video compression and the convolutional 3D network with contextual multiple scales based on the temporal features (Xu et al. in Learning deep representations of appearance and motion for anomalous event detection. In: computer vision and pattern recognition (cvpr), 2015), the paper aims to provide an accurate anomalous human activity recognition system that works in real time. The method is implemented on the UCF101 crime dataset with around 13 different anomalies (Learning Spatiotemporal Features with 3D Convolutional Networks. In: International conference on computer vision (ICCV), ieee, 2015). It resulted in improved recognition performance compared to state-of-the-art techniques. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Adaptive video compression Anomaly detection Human activity recognition vision-based human activity recognition

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Proceedings of the ieee computer society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

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30th ieee conference on computer vision and pattern recognition, cvpr 2017

The proceedings contain 781 papers. The topics discussed include: exclusivity-consistency regularized multi-view subspace clustering;borrowing treasures from the wealthy: deep transfer learning through selective joint fine-tuning;the more you know: using knowledge graphs for image classification;dynamic edge-conditioned filters in convolutional neural networks on graphs;convolutional neural network architecture for geometric matching;deep affordance-grounded sensorimotor object recognition;on compressing deep models by low rank and sparse decomposition;unsupervised pixel-level domain adaptation with generative adversarial networks;photo-realistic single image super-resolution using a generative adversarial network;a practical method for fully automatic intrinsic camera calibration using directionally encoded light;elastic shape-from-template with spatially sparse deforming forces;and distinguishing the indistinguishable: exploring structural ambiguities via geodesic context.

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WiCV 2019: The Sixth Women In computer vision Workshop 32

WiCV 2019: The Sixth Women In Computer Vision Workshop

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Amerini, Irene Balashova, Elena Ebrahimi, Sayna Leonard, Kathryn Nagrani, Arsha Salvador, Amaia Univ Florence Florence Italy Princeton Univ Princeton NJ 08544 USA Univ Calif Berkeley Berkeley CA USA Occident Coll Los Angeles CA USA Univ Oxford Oxford England Univ Politen Catalunya Barcelona Spain

ISBN: (纸本)9781728125060

In this paper we present the Women in computer vision Workshop - WiCV 2019, organized in conjunction with cvpr 2019. This event is meant for increasing the visibility and inclusion of women researchers in computer vision field. computer vision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in the academia and in the industry. WiCV is organized especially for this reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.

关键词： computer vision

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Isospectralization, or how to hear shape, style, and correspondence 32

Isospectralization, or how to hear shape, style, and corresp...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cosmo, Luca Panine, Mikhail Rampini, Arianna Ovsjanikov, Maks Bronstein, Michael M. Rodola, Emanuele Univ Venice Venice Italy Ecole Polytech Palaiseau France Sapienza Univ Rome Rome Italy USI Imperial Coll London London England

ISBN: (纸本)9781728132938

The question whether one can recover the shape of a geometric object from its Laplacian spectrum ('hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications. While theoretically the answer to this question is negative (there exist examples of iso-spectral but non-isometric manifolds), little is known about the practical possibility of using the spectrum for shape reconstruction and optimization. In this paper we introduce a numerical procedure called isospectralization, consisting of deforming one shape to make its Laplacian spectrum match that of another We implement the isospectralization procedure using modern differentiable programming techniques and exemplify its applications in some of the classical and notoriously hard problems in geometry processing, computer vision, and graphics such as shape reconstruction,pose and style transfer and dense deformable correspondence.

关键词： Grouping and Shape Physics-based vision and Shape-from-X Segmentation vision + Graphics

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Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising 32

Non-local Meets Global: An Integrated Paradigm for Hyperspec...

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

作者： He, Wei Yao, Quanming Li, Chao Yokoya, Naoto Zhao, Qibin RIKEN AIP Tokyo Japan HKUST Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) denoising. Unfortunately, while their denoising performance benefits little from more spectral bands, the running time of these methods significantly increases. In this paper, we claim that the HSI lies in a global spectral low-rank subspace, and the spectral subspaces of each full band patch groups should lie in this global low-rank subspace. This motivates us to propose a unified spatial-spectral paradigm for HSI denoising. As the new model is hard to optimize, An efficient algorithm motivated by alternating minimization is developed. This is done by first learning a low-dimensional orthogonal basis and the related reduced image from the noisy HSI. Then, the non-local low-rank denoising and iterative regularization are developed to refine the reduced image and orthogonal basis, respectively. Finally, the experiments on synthetic and both real datasets demonstrate the superiority against the stateof-the-art HSI denoising methods.

关键词： Low-level vision vision + Graphics

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Recognizing Multi-Modal Face Spoofing with Face recognition Networks 32

Recognizing Multi-Modal Face Spoofing with Face Recognition ...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Parkin, Aleksandr Grinchuk, Oleg VisionLabs Amsterdam Netherlands

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125060

Detecting spoofing attacks plays a vital role for deploying automatic face recognition for biometric authentication in applications such as access control, face payment, device unlock, etc. In this paper we propose a new anti-spoofing network architecture that takes advantage of multi-modal image data and aggregates intra-channel features at multiple network layers. We also transfer strong facial features learned for face recognition and show their benefits for detecting spoofing attacks. Finally, to increase the generalization ability of our method to unseen attacks, we use an ensemble of models trained separately for distinct types of spoofing attacks. The proposed method achieves state-of-the-art result on the largest multi-modal anti-spoofing dataset CASIA-SURF [26].

关键词： Face Face recognition Task analysis Training Cameras Training data Neural networks

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