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Improved object recognition using neural networks trained to mimic the brain's statistical properties

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arXiv 2019年

作者： Federer, Callie Fyshe, Alona Xu, Haoyan Zylberberg, Joel Department of Physiology and Biophysics University of Colorado Anschutz Medical Campus AuroraCO United States Department of Computer Science University of Alberta EdmontonAB Canada Center for Vision Research York University TorontoON Canada

The current state-of-the-art object recognition algorithms, deep convolutional neural networks (DCNNs), are inspired by the architecture of the mammalian visual system [8], and capable of human-level performance on many tasks [15]. However, even these algorithms make errors. As DCNNs improve at object recognition tasks, they develop representations in their hidden layers that become more similar to those observed in the mammalian brains [24]. This led us to hypothesize that teaching DCNNs to achieve even more brain-like representations could improve their performance. To test this, we trained DCNNs on a composite task, wherein networks were trained to: a) classify images of objects;while b) having intermediate representations that resemble those observed in neural recordings from monkey visual cortex. Compared with DCNNs trained purely for object categorization, DCNNs trained on the composite task had better object recognition performance. Our results outline a new way to regularize object recognition networks, using transfer learning strategies in which the brain serves as a teacher for training DCNNs. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

Corrigendum to "PSFHS challenge report: pubic symphysis and fetal head segmentation from intrapartum ultrasound images" [Medical Image Analysis 99 (2025),103353]

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Medical image analysis 2025年 104卷 103651页

作者： Jieyun Bai Zihao Zhou Zhanhong Ou Gregor Koehler Raphael Stock Klaus Maier-Hein Marawan Elbatel Robert Martí Xiaomeng Li Yaoyang Qiu Panjie Gou Gongping Chen Lei Zhao Jianxun Zhang Yu Dai Fangyijie Wang Guénolé Silvestre Kathleen Curran Hongkun Sun Jing Xu Pengzhou Cai Lu Jiang Libin Lan Dong Ni Mei Zhong Gaowen Chen Víctor M Campello Yaosheng Lu Karim Lekadir Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand. Electronic address: jbai996@aucklanduni.ac.nz. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Computer Science and Engineering The Chinese University of Hong Kong China. Computer Vision and Robotics Group University of Girona Girona Spain. Canon Medical Systems (China) Co. LTD Beijing China. College of Artificial Intelligence Nankai University Tianjin China. College of Computer Science and Electronic Engineering Hunan University Changsha China. School of Medicine University College Dublin Dublin Ireland. School of Computer Science University College Dublin Dublin Ireland. School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China. School of Computer Science & Engineering Chongqing University of Technology Chongqing China. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound & Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging & School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China. NanFang Hospital of Southern Medical University Guangzhou China. Zhujiang Hospital of Southern Medical University Guangzhou China. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain. Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China. Electronic address: tluys@***. Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain.

The First Visual Object Tracking Segmentation VOTS2023 Challenge Results

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The First Visual Object Tracking Segmentation VOTS2023 Chall...

International Conference on computer vision Workshops (ICCV Workshops)

作者： Matej Kristan Jiří Matas Martin Danelljan Michael Felsberg Hyung Jin Chang Luka Čehovin Zajc Alan Lukežič Ondrej Drbohlav Zhongqun Zhang Khanh-Tung Tran Xuan-Son Vu Johanna Björklund Christoph Mayer Yushan Zhang Lei Ke Jie Zhao Gustavo Fernández Noor Al-Shakarji Dong An Michael Arens Stefan Becker Goutam Bhat Sebastian Bullinger Antoni B. Chan Shijie Chang Hanyuan Chen Xin Chen Yan Chen Zhenyu Chen Yangming Cheng Yutao Cui Chunyuan Deng Jiahua Dong Matteo Dunnhofer Wei Feng Jianlong Fu Jie Gao Ruize Han Zeqi Hao Jun-Yan He Keji He Zhenyu He Xiantao Hu Kaer Huang Yuqing Huang Yi Jiang Ben Kang Jin-Peng Lan Hyungjun Lee Chenyang Li Jiahao Li Ning Li Wangkai Li Xiaodi Li Xin Li Pengyu Liu Yue Liu Huchuan Lu Bin Luo Ping Luo Yinchao Ma Deshui Miao Christian Micheloni Kannappan Palaniappan Hancheol Park Matthieu Paul HouWen Peng Zekun Qian Gani Rahmon Norbert Scherer-Negenborn Pengcheng Shao Wooksu Shin Elham Soltani Kazemi Tianhui Song Rainer Stiefelhagen Rui Sun Chuanming Tang Zhangyong Tang Imad Eddine Toubal Jack Valmadre Joost van de Weijer Luc Van Gool Jash Vira Stèphane Vujasinović Cheng Wan Jia Wan Dong Wang Fei Wang Feifan Wang He Wang Limin Wang Song Wang Yaowei Wang Zhepeng Wang Gangshan Wu Jiannan Wu Qiangqiang Wu Xiaojun Wu Anqi Xiao Jinxia Xie Chenlong Xu Min Xu Tianyang Xu Yuanyou Xu Bin Yan Dawei Yang Ming-Hsuan Yang Tianyu Yang Yi Yang Zongxin Yang Xuanwu Yin Fisher Yu Hongyuan Yu Qianjin Yu Weichen Yu YongSheng Yuan Zehuan Yuan Jianlin Zhang Lu Zhang Tianzhu Zhang Guodongfang Zhao Shaochuan Zhao Yaozong Zheng Bineng Zhong Jiawen Zhu Xuefeng Zhu Yueting Zhuang ChengAo Zong Kunlong Zuo University of Ljubljana Slovenia Czech Technical University Czech Republic ETH Zürich Switzerland Linköping University Sweden University of Birmingham United Kingdom Umeå University Sweden Dalian University of Technology China Austrian Institute of Technology Austria University of Missouri USA Institute of Automation China Fraunhofer IOSB Germany City University of Hong Kong Hong Kong China DAMO Academy China HuaQiao University China Zhejiang University China Nanjing University China Georgia Institute of Technology USA Shenyang Institute of Automation Chinese Academia of Science China University of Udine Italy Tianjin University China Microsoft Research Asia China Harbin Institute of Technology (Shenzhen) China Guangxi Normal University China Lenovo Research China ByteDance China Nota AI South Korea University of Science and Technology of China China Peng Cheng Laboratory China Jiangnan University China The University of Hong Kong Hong Kong China Microsoft Research China Karlsruhe Institute of Technology (KIT) Germany University of Chinese Academy of Sciences China University of Adelaide Australia Computer Vision Center Spain Boston College USA University of South Carolina USA Carnegie Mellon University USA University of California at Merced USA International Digital Economy Academy China Multimedia Department Xiaomi Inc China Institute of Computing Technology Chinese Academy of Sciences China

The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website 1 .

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Gaussian curvature filter on 3d mesh

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arXiv 2020年

作者： Tang, Wenming Gong, Yuanhao Liu, Kanglin Liu, Jun Pan, Wei Liu, Bozhi Qiu, Guoping College of Information Engineering Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Mechanical & Automotive Engineering South China University of Technology Department of Research and Development OPT Machine Vision Tech Co. Ltd Jinsheng Road Changan Dongguan Guangdong523860 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Minimizing Gaussian curvature of meshes is fundamentally important for obtaining smooth and developable surfaces. However, there is a lack of computationally efficient and robust Gaussian curvature optimization method. In this paper, we present a simple yet effective method that can efficiently reduce Gaussian curvature for 3D meshes. We first present the mathematical foundation of our method, which states that for any point on a developable surface there must be another point that lives on its tangent plane. Then, based on this theoretical insight, we introduce a simple and robust implicit Gaussian curvature optimization method named Gaussian Curvature Filter (GCF). GCF implicitly minimizes Gaussian curvature without the need to explicitly calculate the Gaussian curvature itself. GCF is highly efficient and is 20 times faster than the classical standard Gaussian curvature flow method. We present extensive experiments to demonstrate that GCF significantly outperforms state-of-the-art methods in minimizing Gaussian curvature, geometric feature preserving smoothing and mesh noise removal. This method can be used in a large range of applications that involve Gaussian curvature. Copyright © 2020, The Authors. All rights reserved.

关键词： Mesh generation

Correction: A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients

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EJNMMI physics 2023年第1期10卷 13页

作者： Sonia Baeza Debora Gil Ignasi Garcia-Olivé Maite Salcedo-Pujantell Jordi Deportós Carles Sanchez Guillermo Torres Gloria Moragas Antoni Rosell Respiratory Medicine Department Hospital Universitari Germans Trias I Pujol Badalona Barcelona Spain. smbaeza.germanstrias@gencat.cat. Germans Trias I Pujol Research Institute (IGTP) Badalona Barcelona Spain. smbaeza.germanstrias@gencat.cat. Departament de Medicina Universitat Autónoma de Barcelona Barcelona Spain. smbaeza.germanstrias@gencat.cat. Computer Vision Center and Computer Science Department UAB Barcelona Spain. Respiratory Medicine Department Hospital Universitari Germans Trias I Pujol Badalona Barcelona Spain. Germans Trias I Pujol Research Institute (IGTP) Badalona Barcelona Spain. Departament de Medicina Universitat Autónoma de Barcelona Barcelona Spain. Nuclear Medicine Department Hospital Universitari Germans Trias I Pujol Badalona Barcelona Spain.

BenthicNet: A global compilation of seafloor images for deep learning applications

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arXiv 2024年

作者： Lowe, Scott C. Misiuk, Benjamin Xu, Isaac Abdulazizov, Shakhboz Baroi, Amit R. Bastos, Alex C. Best, Merlin Ferrini, Vicki Friedman, Ariell Hart, Deborah Hoegh-Guldberg, Ove Ierodiaconou, Daniel Mackin-McLaughlin, Julia Markey, Kathryn Menandro, Pedro S. Monk, Jacquomo Nemani, Shreya O’Brien, John Oh, Elizabeth Reshitnyk, Luba Y. Robert, Katleen Roelfsema, Chris M. Sameoto, Jessica A. Schimel, Alexandre C.G. Thomson, Jordan A. Wilson, Brittany R. Wong, Melisa C. Brown, Craig J. Trappenberg, Thomas Vector Institute TorontoON Canada Memorial University of Newfoundland Department of Geography St. John’sNL Canada Memorial University of Newfoundland Department of Earth Sciences St. John’sNL Canada Dalhousie University Faculty of Computer Science HalifaxNS Canada Dalhousie University School for Resource and Environmental Studies HalifaxNS Canada Dalhousie University DeepSense HalifaxNS Canada Universidade Federal do Espírito Santo Departamento de Oceanografia e Ecologia Vitória Brazil Fisheries and Oceans Canada Marine Spatial Ecology and Analysis Section Institute of Ocean Sciences SidneyBC Canada Columbia University Lamont-Doherty Earth Observatory PalisadesNY United States University of Sydney Australian Centre for Field Robotics SydneyNSW Australia Greybits Engineering SydneyNSW Australia National Oceanic and Atmospheric Administration Northeast Fisheries Science Center Woods HoleMA United States University of Queensland School of the Environment BrisbaneQLD Australia Deakin University School of Life and Environmental Sciences WarrnamboolVIC Australia Oxy Occidental College Vantuna Research Group Los AngelesCA United States University of Tasmania Institute for Marine and Antarctic Studies HobartTAS Australia Fisheries and Marine Institute of Memorial University of Newfoundland School of Ocean Technology St. John’sNL Canada Fisheries and Oceans Canada Bedford Institute of Oceanography DartmouthNS Canada Hakai Institute Heriot Bay BC Canada Trondheim Norway Ecology Action Centre HalifaxNS Canada Dalhousie University Department of Oceanography HalifaxNS Canada

Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 3.1 million annotations translated to the CATAMI scheme, which span 190 000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for reuse at doi:10.20383/103.0614. © 2024, CC BY.

关键词： Underwater imaging

Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms

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arXiv 2021年

作者： Poh, Norman Bourlai, Thirimachos Kittler, Josef Allano, Lorene Alonso-Fernandez, Fernando Ambekar, Onkar Baker, John Dorizzi, Bernadette Fatukasi, Omolara Fierrez, Julian Ganster, Harald Ortega-Garcia, Javier Maurer, Donald Salah, Albert Ali Scheidat, Tobias Vielhauer, Claus Centre for Vision Speech and Signal Processing School of Electronics and Physical Sciences University of Surrey Guildford SurreyGU2 7XH United Kingdom Biometrics Center Lane Department of Computer Science and Electrical Engineering College of Engineering and Mineral Resources West Virginia University MorgantownWV26506-6109 United States Institut Telecom Telecom and Management SudParis Evry91011 France Biometric Recognition Group – ATVS Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid28049 Spain Amsterdam1098 XG Netherlands Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Electronics and Physics Department Institut Telecom Telecom and Management SudParis Evry91011 France Institute of Digital Image Processing Joanneum Research Graz8010 Austria ISLA-ISIS University of Amsterdam Amsterdam1098 XG Netherlands Department of Informatics and Media Brandenburg University of Applied Sciences Brandenburg an der HavelD-14770 Germany Research Group Multimedia and Security Department of Technical and Business Information Systems Faculty of Computer Science Otto-von-Guericke-University of Magdeburg MagdeburgD-39106 Germany

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing

关键词： Benchmarking

Asymmetric CNN for image super-resolution

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arXiv 2021年

作者： Tian, Chunwei Xu, Yong Zuo, Wangmeng Lin, Chia-Wen Zhang, David The Bio-Computing Research Center Harbin Institute of Technology ShenzhenShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China The School of Computer Science and Technology Harbin Institute of Technology HarbinHeilongjiang150001 China The Peng Cheng Laboratory ShenzhenGuangdong518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen Guangdong518055 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan ShenzhenGuangdong518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. According to nature of different applications, designing appropriate CNN architectures is developed. However, customized architectures gather different features via treating all pixel points as equal to improve the performance of given application, which ignores the effects of local power pixel points and results in low training efficiency. In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a memory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. The AB utilizes one-dimensional asymmetric convolutions to intensify the square convolution kernels in horizontal and vertical directions for promoting the influences of local salient features for SISR. The MEB fuses all hierarchical low-frequency features from the AB via residual learning (RL) technique to resolve the long-term dependency problem and transforms obtained low-frequency features into high-frequency features. The HFFEB exploits low- and high-frequency features to obtain more robust super-resolution features and address excessive feature enhancement problem. Additionally, it also takes charge of reconstructing a high-resolution (HR) image. Extensive experiments show that our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems. The code of the ACNet is shown at https://***/hellloxiaotian/ACNet. © 2021, CC BY-NC-ND.

关键词： Image enhancement

What catches the eye? Visualizing and understanding deep saliency models

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arXiv 2018年

作者： He, Sen Borji, Ali Mi, Yang Pugeault, Nicolas Department of Computer Science University of Exeter Center for Research in Computer Vision University of Central Florida

Deep convolutional neural networks have demonstrated high performances for fixation prediction in recent years. How they achieve this, however, is less explored and they remain to be black box models. Here, we attempt to shed light on the internal structure of deep saliency models and study what features they extract for fixation prediction. Specifically, we use a simple yet powerful architecture, consisting of only one CNN and a single resolution input, combined with a new loss function for pixel-wise fixation prediction during free viewing of natural scenes. We show that our simple method is on par or better than state-of-the-art complicated saliency models. Furthermore, we propose a method, related to saliency model evaluation metrics, to visualize deep models for fixation prediction. Our method reveals the inner representations of deep models for fixation prediction and provides evidence that saliency, as experienced by humans, is likely to involve high-level semantic knowledge in addition to low-level perceptual cues. Our results can be useful to measure the gap between current saliency models and the human inter-observer model and to build new models to close this gap. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting