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arXiv 2024年

作者： Cioppa, Anthony Giancola, Silvio Somers, Vladimir Joos, Victor Magera, Floriane Held, Jan Ghasemzadeh, Seyed Abolfazl Zhou, Xin Seweryn, Karolina Kowalczyk, Mateusz Mróz, Zuzanna Lukasik, Szymon Halon, Michal Mkhallati, Hassan Deliège, Adrien Hinojosa, Carlos Sanchez, Karen Mansourian, Amir M. Miralles, Pierre Barnich, Olivier De Vleeschouwer, Christophe Alahi, Alexandre Ghanem, Bernard Van Droogenbroeck, Marc Gorski, Adam Clapés, Albert Boiarov, Andrei Afanasiev, Anton Xarles, Artur Scott, Atom Lim, ByoungKwon Yeung, Calvin Gonzalez, Cristian Rüfenacht, Dominic Pacilio, Enzo Deuser, Fabian Altawijri, Faisal Sami Cachón, Francisco Kim, HanKyul Wang, Haobo Choe, Hyeonmin Kim, Hyunwoo J. Kim, Il-Min Kang, Jae-Mo Tursunboev, Jamshid Yang, Jian Hong, Jihwan Lee, Jimin Zhang, Jing Lee, Junseok Zhang, Kexin Habel, Konrad Jiao, Licheng Li, Linyi Gutiérrez-Pérez, Marc Ortega, Marcelo Li, Menglong Lopatto, Milosz Kasatkin, Nikita Nemtsev, Nikolay Oswald, Norbert Udin, Oleg Kononov, Pavel Geng, Pei Alotaibi, Saad Ghazai Kim, Sehyung Ulasen, Sergei Escalera, Sergio Zhang, Shanshan Yang, Shuyuan Moon, Sunghwan Moeslund, Thomas B. Shandyba, Vasyl Golovkin, Vladimir Dai, Wei Chung, WonTaek Liu, Xinyu Zhu, Yongqiang Kim, Youngseo Li, Yuan Yang, Yuting Xiao, Yuxuan Cheng, Zehua Li, Zhihao Belgium King Abdullah University of Science and Technology Saudi Arabia Sportradar Switzerland UCLouvain Belgium EPFL Switzerland EVS Broadcast Equipment Belgium Baidu Research United States NASK National Research Institute Poland Belgium Sharif University of Technology Iran Footovision France Mobius Labs Institut de Robòtica i Informàtica Industrial CSIC-UPC Spain TAHAKOM Saudi Arabia AIBrain Eidos.ai United Kingdom School of Computer Science and Engineering Nanjing University of Science and Technology China Constructor.tech Universitat de Barcelona Spain Computer Vision Center Spain Aalborg University Denmark Kyungpook National University Korea Republic of Intelligent Perception and Image Understanding Lab Xidian University China Germany Korea University Korea Republic of Warsaw University of Technology Poland University of Oxford United Kingdom China Science IntelliCloud Technology Co. Ltd China Robo Space Graduate School of Informatics Nagoya University Japan Tongji University China AGH University of Krakow Poland

The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team. These challenges aim to advance research across multiple themes in football, including broadcast video understanding, field understanding, and player understanding. This year, the challenges encompass four vision-based tasks. (1) Ball Action Spotting, focusing on precisely localizing when and which soccer actions related to the ball occur, (2) Dense Video Captioning, focusing on describing the broadcast with natural language and anchored timestamps, (3) Multi-View Foul Recognition, a novel task focusing on analyzing multiple viewpoints of a potential foul incident to classify whether a foul occurred and assess its severity, (4) Game State Reconstruction, another novel task focusing on reconstructing the game state from broadcast videos onto a 2D top-view map of the field. Detailed information about the tasks, challenges, and leaderboards can be found at https://***, with baselines and development kits available at https://***/SoccerNet. © 2024, CC BY.

关键词： HTTP

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Fical: Focal inter-class angular loss for image classification 34

Fical: Focal inter-class angular loss for image classificati...

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34th IEEE International Conference on Visual Communications and image Processing, VCIP 2019

作者： Wei, Xinran Chang, Dongliang Xie, Jiyang Zheng, Yixiao Gong, Chen Zhang, Chuang Ma, Zhanyu Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent Systems Lab Beijing China Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education PCA Lab China Jiangsu Key Lab of Image and Video Understanding for Social Security China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781728137230

Convolutional Neural Networks (CNNs) have been successfully applied in various image analysis tasks and gradually become one of the most powerful machine learning approaches. In order to improve the capability of the model generalization and performance in image classification, a new trend is to learn more discriminative features via CNNs. The main contribution of this paper is to increase the angles between the categories to extract discriminative features and enlarge the inter-class variance. To this end, we propose a loss function named focal inter-class angular loss (FICAL) which introduces the confusion rate-weighted cosine distance as the similarity measurement between categories. This measurement is dynamically evaluated during each iteration to adapt the model. Compared with other loss functions, experimental results demonstrate that the proposed FICAL achieved best performance among the referred loss functions on two image classificaton datasets. © 2019 IEEE.

关键词： image classification

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Generalized focal loss V2: Learning reliable localization quality estimation for dense object detection

arXiv

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arXiv 2020年

作者： Li, Xiang Wang, Wenhai Hu, Xiaolin Li, Jun Tang, Jinhui Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China Nanjing University of Science and Technology China Momenta China Nanjing University China Tsinghua University China

Localization Quality Estimation (LQE) is crucial and popular in the recent advancement of dense object detectors since it can provide accurate ranking scores that benefit the Non-Maximum Suppression processing and improve detection performance. As a common practice, most existing methods predict LQE scores through vanilla convolutional features shared with object classification or bounding box regression. In this paper, we explore a completely novel and different perspective to perform LQE – based on the learned distributions of the four parameters of the bounding box. The bounding box distributions are inspired and introduced as "General Distribution" in GFLV1, which describes the uncertainty of the predicted bounding boxes well. Such a property makes the distribution statistics of a bounding box highly correlated to its real localization quality. Specifically, a bounding box distribution with a sharp peak usually corresponds to high localization quality, and vice versa. By leveraging the close correlation between distribution statistics and the real localization quality, we develop a considerably lightweight Distribution-Guided Quality Predictor (DGQP) for reliable LQE based on GFLV1, thus producing GFLV2. To our best knowledge, it is the first attempt in object detection to use a highly relevant, statistical representation to facilitate LQE. Extensive experiments demonstrate the effectiveness of our method. Notably, GFLV2 (ResNet-101) achieves 46.2 AP at 14.6 FPS, surpassing the previous state-of-the-art ATSS baseline (43.6 AP at 14.6 FPS) by absolute 2.6 AP on COCO test-dev, without sacrificing the efficiency both in training and inference. Codes are available at https://***/implus/GFocalV2. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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94 GHz Three-Dimensional Imaging Radar for Environmental Detection 8

94 GHz Three-Dimensional Imaging Radar for Environmental Det...

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8th Asia-Pacific Conference on Antennas and Propagation, APCAP 2019

作者： Wu, HongMing Qi, Feng Wang, Jinkuan Northeastern University College of Information Science and Engineering Shenyang110819 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China

ISBN: (纸本)9781665400541

At present, With the development of technology, the high resolution of millimeter wave (terahertz) can be applied to environmental sensing imaging. In this paper, Using the frequency-modulated continuous-wave (FMCW) radar technique with a 1GHz bandwidth, we show experimentally that a high-resolution real time imaging radar operating at 93.5-94.5GHz is capable of detecting different car models at standoff ranges of 12 m. We also achieved 3D high-resolution imaging of the building. at 140 meters. © 2019 IEEE.

关键词： Millimeter waves

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Integrating Dense LiDAR-Camera Road Detection Maps by a Multi-Modal CRF Model

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IEEE Transactions on Vehicular Technology 2019年第12期68卷 11635-11645页

作者： Gu, Shuo Zhang, Yigong Tang, Jinhui Yang, Jian Alvarez, Jose M. Kong, Hui PCA Lab Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Sch. of Comp. Sci. and Engineering Nanjing Univ. of Sci. and Technol. Nanjing 210094 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China

Road detection is an important task in autonomous navigation systems. In this paper, we propose a road detection method via a LiDAR-camera fusion strategy to exploit both the range and color information. The whole system consists of three parts. In the LiDAR based part, we transform the discrete 3D LiDAR point clouds to continuous 2D LiDAR range images and propose a distance-aware height-difference based scanning approach to get the road estimations quickly. In the camera based part, we apply a light-weight transfer learning based road segmentation network. In the LiDAR-camera fusion part, we transform the detection results from LiDAR and camera to dense and binary ones to solve the data imbalance problem and fuse them in a multi-modal conditional random field (MM-CRF) framework. Experiments show that the proposed MM-CRF fusion method can operate in real-time and achieve competitive performance compared with the state-of-the-art road detection algorithms on the KITTI-Road benchmark. © 1967-2012 IEEE.

关键词： KITTI LiDAR-camera fusion road detection

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Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report

arXiv

引用

arXiv 2021年

作者： Ignatov, Andrey Malivenko, Grigory Plowman, David Shukla, Samarth Timofte, Radu Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Wang, Yiran Li, Xingyi Shi, Min Xian, Ke Cao, Zhiguo Du, Jin-Hua Wu, Pei-Lin Ge, Chao Yao, Jiaoyang Tu, Fangwen Li, Bo Yoo, Jung Eun Seo, Kwanggyoon Xu, Jialei Li, Zhenyu Liu, Xianming Jiang, Junjun Chen, Wei-Chi Joya, Shayan Fan, Huanhuan Kang, Zhaobing Li, Ang Feng, Tianpeng Liu, Yang Sheng, Chuannan Yin, Jian Benavides, Fausto T. Computer Vision Lab ETH Zurich Switzerland Ltd AI Witchlabs Switzerland Tencent GY-Lab China Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Nanjing Artificial Intelligence Chip Research Institute of Automation Chinese Academy of Sciences China Black Sesame Technologies Inc. Singapore Singapore Visual Media Lab KAIST Korea Republic of Harbin Institute of Technology China Peng Cheng Laboratory China Multimedia and Computer Vision Laboratory National Cheng Kung University Taiwan Samsung Research UK United Kingdom OPPO Research Institute China ETH Zurich Switzerland

Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly real-time performance on smartphones and IoT platforms. For this, the participants were provided with a new large-scale dataset containing RGB-depth image pairs obtained with a dedicated stereo ZED camera producing high-resolution depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the popular Raspberry Pi 4 platform with a mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving high fidelity results, and are compatible with any Android or Linux-based mobile devices. A detailed description of all models developed in the challenge is provided in this paper. Copyright © 2021, The Authors. All rights reserved.

关键词： computer operating systems

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Minimal Solutions to Relative Pose Estimation From Two Views Sharing a Common Direction With Unknown Focal Length

Minimal Solutions to Relative Pose Estimation From Two Views...

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： Yaqing Ding Jian Yang Jean Ponce Hui Kong Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology INRIA Paris France Département d'informatique de l'ENS ENS CNRS PSL University Paris France

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

We propose minimal solutions to relative pose estimation problem from two views sharing a common direction with unknown focal length. This is relevant for cameras equipped with an IMU (inertial measurement unit), e.g., smart phones, tablets. Similar to the 6-point algorithm for two cameras with unknown but equal focal lengths and 7-point algorithm for two cameras with different and unknown focal lengths, we derive new 4- and 5-point algorithms for these two cases, respectively. The proposed algorithms can cope with coplanar points, which is a degenerate configuration for these 6- and 7-point counterparts. We present a detailed analysis and comparisons with the state of the art. Experimental results on both synthetic data and real images from a smart phone demonstrate the usefulness of the proposed algorithms.

关键词： Cameras Gravity Eigenvalues and eigenfunctions Smart phones computer vision Pose estimation Charge coupled devices

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DONet: Dual-octave network for fast MR image reconstruction

arXiv

引用

arXiv 2021年

作者： Feng, Chun-Mei Yang, Zhanyuan Fu, Huazhu Xu, Yong Yang, Jian Shao, Ling Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China School of Automation Engineering University of Electronic Science and Technology of China 611731 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjiang University of Science and Technology Nanjiang210094 China Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration has long been the subject of research. This is commonly achieved by obtaining multiple undersampled images, simultaneously, through parallel imaging. In this paper, we propose the Dual-Octave Network (DONet), which is capable of learning multi-scale spatialfrequency features from both the real and imaginary components of MR data, for parallel fast MR image reconstruction. More specifically, our DONet consists of a series of Dual-Octave convolutions (Dual-OctConv), which are connected in a dense manner for better reuse of features. In each Dual-OctConv, the input feature maps and convolutional kernels are first split into two components (i.e., real and imaginary), and then divided into four groups according to their spatial frequencies. Then, our Dual-OctConv conducts intra-group information updating and inter-group information exchange to aggregate the contextual information across different groups. Our framework provides three appealing benefits: (i) It encourages information interaction and fusion between the real and imaginary components at various spatial frequencies to achieve richer representational capacity. (ii) The dense connections between the real and imaginary groups in each Dual-OctConv make the propagation of features more efficient by feature reuse. (iii) DONet enlarges the receptive field by learning multiple spatial-frequency features of both the real and imaginary components. Extensive experiments on two popular datasets (i.e., clinical knee and fastMRI), under different undersampling patterns and acceleration factors, demonstrate the superiority of our model in accelerated parallel MR image reconstruction. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Multi-level graph convolutional network with automatic graph learning for hyperspectral image classification

arXiv

引用

arXiv 2020年

作者： Wan, Sheng Gong, Chen Pan, Shirui Yang, Jie Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph construction and image classification as two separate tasks, which often results in suboptimal performance. Another defect of these methods is that they mainly focus on modeling the local pairwise importance between graph nodes while lack the capability to capture the global contextual information of HSI. In this paper, we propose a Multi-level GCN with Automatic Graph Learning method (MGCN-AGL) for HSI classification, which can automatically learn the graph information at both local and global levels. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions, which helps encode the spatial context to form the graph information at local level. Moreover, we utilize multiple pathways for local-level graph convolution, in order to leverage the merits from the diverse spatial context of HSI and to enhance the expressive power of the generated representations. To reconstruct the global contextual relations, our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level. Then inference can be performed along the reconstructed graph edges connecting faraway regions. Finally, the multi-level information is adaptively fused to generate the network output. In this means, the graph learning and image classification can be integrated into a unified framework and benefit each other. Extensive experiments have been conducted on three real-world hyperspectral datasets, which are shown to outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： image classification

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Technique Report of CVPR 2024 PBDL Challenges

arXiv

引用

arXiv 2024年

作者： Lu, Xiaoqiang Jiao, Licheng Liu, Fang Liu, Xu Li, Lingling Ma, Wenping Yang, Shuyuan Xie, Haiyang Zhao, Jian Huang, Shihua Cheng, Peng Shen, Xi Wang, Zheng An, Shuai Zhu, Caizhi Li, Xuelong Chen, Linwei Fu, Ying Zhang, Tao Liu, Yu Yan, Chenggang Wang, Zichun Wang, Qinliang Liu, Yang Yu, Xinyue Jia, Sen Zhang, Junpei Chen, Puhua Chen, Xiang Li, Hao Pan, Jinshan Xie, Chuanlong Chen, Hongming Li, Mingrui Deng, Tianchen Huang, Jingwei Li, Yufeng Wan, Fei Xu, Bingxin Cheng, Jian Liu, Hongzhe Xu, Cheng Zou, Yuxiang Pan, Weiguo Dai, Songyin Jiang, Linyan Song, Bingyi An, Zhuoyu Lei, Haibo Luo, Qing Song, Jie Liu, Yuan Li, Qihang Zhang, Haoyuan Wang, Lingfeng Chen, Wei Luo, Aling Li, Cheng Cao, Jun Chen, Shu Dou, Zifei Liu, Xinyu Zhang, Jing Zhang, Kexin Yang, Yuting Zhang, Liwen Xu, Zhe Gou, Dingyong Li, Cong Xu, Senyan Zhang, Yunkang Jiang, Siyuan Liu, Qinglin Yu, Wei Lv, Xiaoqian Li, Jianing Zhang, Shengping Ji, Xiangyang Zou, Yunhao Chen, Yuanpei Zhang, Yuhan Peng, Weihang Gou, Xuejian Zhao, Shizhan Zhang, Yanzhao Yan, Libo Guo, Yuwei Li, Guoxin Gao, Qiong Che, Chenyue Sun, Long School of Artificial Intelligence Xidian University China School of Computer Science Wuhan University China Intellindust China Beijing Forestry University China China Telecom China Harbin Institute of Technology China Northwestern Polytechnical University China China Telecom China Beijing Institute of Technology China Lishui Institute of Hangzhou Dianzi University China Tsinghua University China Intelligent Perception and Image Understanding Lab Xidian University China Nanjing University of Science and Technology China Shenyang Aerospace University China Dalian University of Technology China Shanghai Jiao Tong University China University of Electronic Science and Technology of China China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China College of Robotics Beijing Union University China Tencent China Sanechips Technology Co. LTD. China Xiaomi Inc. China ZTE Corporation China University of Science and Technology of China China Peking University China Intelligent Science & Technology Academy of CASIC China

The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies. By leveraging the principles of physics to inform and enhance deep learning models, we can develop more robust and accurate vision systems. Physics-based vision aims to invert the processes to recover scene properties such as shape, reflectance, light distribution, and medium properties from images. In recent years, deep learning has shown promising improvements for various vision tasks, and when combined with physics-based vision, these approaches can enhance the robustness and accuracy of vision systems. This technical report summarizes the outcomes of the Physics-Based vision Meets Deep Learning (PBDL) 2024 challenge, held in CVPR 2024 workshop. The challenge consisted of eight tracks, focusing on Low-Light Enhancement and Detection as well as High Dynamic Range (HDR) Imaging. This report details the objectives, methodologies, and results of each track, highlighting the top-performing solutions and their innovative approaches. © 2024, CC BY.

关键词： Machine vision

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