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arXiv 2020年

作者： Zhang, Zhizheng Lan, Cuiling Zeng, Wenjun Chen, Zhibo Chang, Shih-Fu The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Anhui Hefei China Microsoft Research Asia Beijing China Columbia University New YorkNY United States

Person Re-identification (ReID) aims at matching a person of interest across images. In convolutional neural network (CNN) based approaches, loss design plays a vital role in pulling closer features of the same identity and pushing far apart features of different identities. In recent years, triplet loss achieves superior performance and is predominant in ReID. However, triplet loss considers only three instances of two classes in per-query optimization (with an anchor sample as query) and it is actually equivalent to a two-class classification. There is a lack of loss design which enables the joint optimization of multiple instances (of multiple classes) within per-query optimization for person ReID. In this paper, we introduce a multi-class classification loss, i.e., N-tuple loss, to jointly consider multiple (N) instances for per-query optimization. This in fact aligns better with the ReID test/inference process, which conducts the ranking/comparisons among multiple instances. Furthermore, for more efficient multi-class classification, we propose a new meta prototypical N-tuple loss. With the multi-class classification incorporated, our model achieves the state-of-the-art performance on the benchmark person ReID datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Research on Integrated Deployment Method of Digital Earth Thematic application Model 1

Research on Integrated Deployment Method of Digital Earth Th...

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1st China Digital Earth Conference, CDEC 2019

作者： Danpu, Zhao Xianqing, Tai Zhijie, Ma Key Laboratory of Technology in Geospatial Information Processing and Application System Institute of Elec-tronics Chinese Academy of Sciences Beijing100190 China Research Center of Intelligent City and Intelligent Transportation CAS R and D Center for Internet of Things Wuxi214135 China

Nowadays, the digital earth not only relates to the technologies of surveying and mapping geography, but also includes the analysis and cross-application of various scientific data related to geographic information. It requires a computing environment and management for the corresponding thematic application models which supports the online analysis on the digital earth. So we proposed an integrated deployment method for digital earth thematic application models based on container cloud, which provides a flexible and scalable information integration and analysis architecture for digital earth. Firstly, a Docker image building module is designed to facilitate the user to quickly build the application model into a image. Then the Docker technology is used to implement the containerization of the application model to provide a stable running environment for the application model. Finally, the Kubernetes is used to dynamically manage the cluster resources to realize rapid expansion of computing resources. In addition, the HDFS is deployed, and the image files and various scientific data respectively store with Hbase and Apache file systems. It can ensure the read and write speed of the application model and the related data and log files. It also used zabbix framework to manage the monitoring index of the container cloud cluster to ensure the execution effect of the application model. Through the tests of typical application model, it is verified that the method can integrated deploy and apply the cross-domain, hetero-architecture, multi-version and multi-class application models on the digital earth. © Published under licence by IOP Publishing Ltd.

关键词： Containers

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Statistical analysis of typical target regions of multi-Aspect SAR based on parametric model 14

Statistical analysis of typical target regions of multi-Aspe...

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14th European Conference on Synthetic Aperture Radar, EUSAR 2022

作者： Yue, Xiaoyang Teng, Fei Zhang, Zhiguo Lin, Yun Hong, Wen Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing China Aerospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Electronic Information Engineering North China University of Technology Beijing China

ISBN: (纸本)9783800758234

Multi-Aspect SAR can obtain rich backscatter information about the illuminated scene. For different kinds of targets in the scene, scattering information related to the geometric characteristics of the target is shown in the imaging scene at the same angle. For a certain reference target in the scene, it exhibits azimuth-based backscatter characteristics in the imaging scene under observation from different angles. In our article, a parametric statistical model is used to express characteristics of typical targets at different angles and characteristic differences between different targets. Two parameters-And-Are used to represent characteristic difference of typical targets. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Synthetic aperture radar

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Radar image simulation of complex targets

Radar image simulation of complex targets

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6th International Conference on Radar, RADAR 2011

作者： Tang, Kan Zhang, Yueting Chen, Hongzhen Xiong, Wenchang Wang, Hongqi Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences NO. 9 Beiyitiao Zhongguancun Beijing 100190 China National Key Laboratory of Microwave Imaging Technology NO. 19 Beisihuan Xilu Beijing 100190 China Institute of Electronics Chinese Academy of Sciences NO. 19 Beisihuan Xilu Beijing 100190 China

ISBN: (纸本)9781424484416

In this paper, we propose a novel radar imaging simulator with precise simulation of geometry and relative simulation of radiometry, to assist target recognition. The simulator is based on an new visualization procedure and a Lambertian-specular mixture model. Simulated results are compared with MiniSAR image of airplane C-130 evaluating the efficiency. © 2011 IEEE.

关键词： Radar imaging

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Radar imaging simulation for typical urban structures based on analytical models

Radar imaging simulation for typical urban structures based ...

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6th International Conference on Radar, RADAR 2011

作者： Chen, Hongzhen Zhang, Yueting Tang, Kan Xiong, Wenchang Ding, Chibiao Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences NO. 9 Beiyitiao Zhongguancun Beijing 100190 China National Key Laboratory of Microwave Imaging Technology NO.19 Beisihuan Xilu Beijing 100190 China Institute of Electronics Chinese Academy of Sciences NO.19 Beisihuan Xilu Beijing 100190 China

ISBN: (纸本)9781424484416

This paper proposes a novel radar imaging simulator based on analytical electromagnetic and geometric models for a common typical kind of urban structure. The analytical models are presented not only for improving the efficiency of the simulation but also for direct and further understanding the features in the SAR image. © 2011 IEEE.

关键词： Radar imaging

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OBJECT DETECTION WITH HEAD DIRECTION IN REMOTE SENSING IMAGES BASED ON ROTATIONAL REGION CNN

OBJECT DETECTION WITH HEAD DIRECTION IN REMOTE SENSING IMAGE...

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IEEE International geoscience and Remote Sensing Symposium

作者： Xue Yang Kun Fu Hao Sun Xian Sun Menglong Yan Wenhui Diao Zhi Guo Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

Object detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. In this paper, we propose a novel detection framework based on rotational region convolution neural network to cope with the problem of non-maximum suppression in dense objects detection. The bounding boxes obtained by adopting our method is the minimum bounding rectangle of object with less redundant regions. Furthermore, we find the head direction of the object through prediction. There are three important changes to our framework over traditional detection methods, representation and regression of rotational bounding box, head direction prediction and rotational non-maximal suppression. Experiments based on remote sensing images from Google Earth for Object detection show that our detection method based on rotational region CNN has a competitive performance.

关键词： Object detection Rotating region Convolution neural network Prediction Non-maximal suppression

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DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning

arXiv

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arXiv 2022年

作者： Zhao, Youpeng Zhao, Jian Hu, Xunhan Zhou, Wengang Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various perfect and imperfect information games. Among these games, DouDizhu, a popular card game in China, is very challenging due to the imperfect information, large state space, elements of collaboration and a massive number of possible moves from turn to turn. Recently, a DouDizhu AI system called DouZero has been proposed. Trained using traditional Monte Carlo method with deep neural networks and self-play procedure without the abstraction of human prior knowledge, DouZero has outperformed all the existing DouDizhu AI programs. In this work, we propose to enhance DouZero by introducing opponent modeling into DouZero. Besides, we propose a novel coach network to further boost the performance of DouZero and accelerate its training process. With the integration of the above two techniques into DouZero, our DouDizhu AI system achieves better performance and ranks top in the Botzone leaderboard among more than 400 AI agents, including DouZero. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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An improved method to sort and pair TDOA based on the correlation between TDOAs

An improved method to sort and pair TDOA based on the correl...

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6th International Conference on Radar, RADAR 2011

作者： Ren, Wen-Juan Hu, Dong-Hui Ding, Chi-Biao Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Graduate School Chinese Academy of Sciences No. 19 North 4yh Ring Road West Haidian District Beijing 100190 China

ISBN: (纸本)9781424484416

In TDOA passive location system, the situations in which false TDOA combinations arose are sorted. For extracting the correct TDOA combination of the radiant source, an improved method to sort and pair TDOA is proposed in this paper. The method can eliminate the false TDOA combinations which are reduced by sub-PRI(pulse repetition interval) of the radiant source with high pulse repetition frequency(PRF) and the equality of multiple targets' TDOAs, and associate all TDOA combinations which belong to the same target. The method enhances the accuracy of extracting TDOA combination and reduces the computation load and the complexity of afterward processing. © 2011 IEEE.

关键词： Time difference of arrival

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DA-RefineNet: Dual-inputs Attention RefineNet for Whole Slide Image Segmentation

DA-RefineNet: Dual-inputs Attention RefineNet for Whole Slid...

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International Conference on Pattern Recognition

作者： Ziqiang Li Rentuo Tao Qianrun Wu Bin Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems University of Science and Technology of China Hefei Anhui China School of foreign studies Hefei University of Technology Hefei Anhui China

Automatic medical image segmentation has wide applications for disease diagnosing. However, it is much more challenging than natural optical image segmentation due to the high-resolution of medical images and the corresponding huge computation cost. The sliding window is a commonly used technique for whole slide image (WSI) segmentation, however, for these methods based on the sliding window, the main drawback is lacking global contextual information for supervision. In this paper, we propose a dual-inputs attention network (denoted as DA-RefineNet) for WSI segmentation, where both local fine-grained information and global coarse information can be efficiently utilized. Sufficient comparative experiments are conducted to evaluate the effectiveness of the proposed method, the results prove that the proposed method can achieve better performance on WSI segmentation compared to methods relying on single-input.

关键词： Knowledge engineering Image segmentation Semantics Breast Feature extraction Optical imaging Pattern recognition

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RELAXATION LABELLING BASED LAND MASKING IN SAR IMAGES

RELAXATION LABELLING BASED LAND MASKING IN SAR IMAGES

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IEEE International geoscience and Remote Sensing Symposium

作者： Zongxu Pan Lei Liu Bin Lei Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China

In this paper, a relaxation labelling based land masking method is proposed for separating sea and land in SAR images. Land masking, also known as sea-land segmentation, is a part of ship detection system for SAR images to avoid detecting false alarms in the land. Relaxation labeling is an iterative method, which can separate foreground pixels from background ones using the neighborhood information of pixels in the image. When relaxation labelling converges, the segmented result is often unsatisfactory, since it tends to label more foreground pixels. To overcome this issue, a loss composed of the background probability distribution diversity and the gradient magnitude of the result is introduced to indicate when to stop the iteration. Experimental results on several Gaofen-3 SAR images demonstrate the effectiveness of the proposed method.

关键词： Labeling Image segmentation Synthetic aperture radar Marine vehicles Probability distribution Morphology Iterative methods

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