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A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing [article]

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie
2020 arXiv   pre-print
With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems.  ...  Few-shot learning can rapidly generalize to new tasks of limited supervised experience by turning to prior knowledge, which mimics human's ability to acquire knowledge from few examples through generalization  ...  Similarly, two different neural networks were developed in [230] with spectrogram of the time domain waveform by STFT for radar emitter recognition.  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 8028-8042 A Two-Stage Approach to Few-Shot Learning for Image Recognition. An Unordered Image Stitching Method Based on Binary Tree and Estimated Overlapping Area.  ...  ., +, TIP 2020 5447-5456 A Two-Stage Approach to Few-Shot Learning for Image Recognition. Das, D., +, TIP 2020 3336-3350 A Unified Probabilistic Formulation of Image Aesthetic Assessment.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TIP 2021 2288-2300 A Pairwise Attentive Adversarial Spatiotemporal Network for Cross-Domain Few-Shot Action Recognition-R2.  ...  ., +, TIP 2021 5402-5412 A Pairwise Attentive Adversarial Spatiotemporal Network for Cross-Domain Few-Shot Action Recognition-R2.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Multi-sized Object Detection Using Spaceborne Optical Imagery

Muhammad Haroon, Muhammad Shahzad, Muhammad Moazam Fraz
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
dataset (SIMD) along with an adapted single pass deep multiscale object detection framework with the aim to detect multisized/type objects for catering above-ground perspective of vehicles.  ...  To ignite further research in this domain, the introduced SIMD dataset and the corresponding architecture is publicly available at this link: http://vision.seecs.edu.pk/simd.  ...  This dataset contains only two vehicle classes, i.e., large vehicles for trucks and buses and small vehicles for cars and vans.  ... 
doi:10.1109/jstars.2020.3000317 fatcat:fpzak4mnwfci7gjewfknh57etu

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Khaled Bayoudh, Raja Knani, Fayçal Hamdaoui, Abdellatif Mtibaa
2021 The Visual Computer  
The growing potential of multimodal data streams and deep learning algorithms has contributed to the increasing universality of deep multimodal learning.  ...  We also survey current multimodal applications and present a collection of benchmark datasets for solving problems in various vision domains.  ...  Deep learning methods Deep belief networks based Deep belief network (DBN) is part of the graphical generative deep model [15] .  ... 
doi:10.1007/s00371-021-02166-7 pmid:34131356 pmcid:PMC8192112 fatcat:jojwyc6slnevzk7eaiutlmlgfe

Table of contents

2021 ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
, China; Lingbo Liu, Sun Yat-Sen University, China; Jun Hou, Sensetime, China; Ping Wang, Peking University, China IVMSP-9.3: KAN: KNOWLEDGE-AUGMENTED NETWORKS FOR FEW-SHOT ............................  ...  , China IVMSP-9.6: DOMAIN ADAPTATION FOR LEARNING GENERATOR FROM PAIRED .................................... 2340 FEW-SHOT DATA Chun-Chih Teng, National Chiao Tung University, Taiwan; Pin-Yu Chen, IBM  ... 
doi:10.1109/icassp39728.2021.9414617 fatcat:m5ugnnuk7nacbd6jr6gv2lsfby

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision [article]

Jinli Suo, Weihang Zhang, Jin Gong, Xin Yuan, David J. Brady, Qionghai Dai
2021 arXiv   pre-print
Differently, Computational Imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems.Thanks to AI, CI can now be used  ...  in real systems by integrating deep learning algorithms into the mobile vision platform to achieve the closed loop of intelligent acquisition, processing and decision making, thus leading to the next  ...  Most recently, deep learning has been used in the single-shot HDR system design [88] , [89] .  ... 
arXiv:2109.08880v1 fatcat:nbwyxsweljagpb43nzwpk3lf7y

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging on Arbitrary Region of Interest; TGRS  ...  ., Geosynchronous SAR Tomography: Theory and First Experimental Verification Using Beidou IGSO Satellite; TGRS Sept. 2019 6591-6607 Hu, F., Wu, J., Chang, L., and Hanssen, R.F., Incorporating Temporary  ...  ., +, TGRS Aug. 2019 5373-5383 Deep Few-Shot Learning for Hyperspectral Image Classification.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

A Survey of Deep Learning-based Object Detection

Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhixi Feng, Rong Qu
2019 IEEE Access  
With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved.  ...  Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors.  ...  [312] introduce bilinear models that consists of two feature extractors (two CNN streams).  ... 
doi:10.1109/access.2019.2939201 fatcat:jesz2av2tjbkxfpaqyecptgls4

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Stamatios Samaras, Eleni Diamantidou, Dimitrios Ataloglou, Nikos Sakellariou, Anastasios Vafeiadis, Vasilis Magoulianitis, Antonios Lalas, Anastasios Dimou, Dimitrios Zarpalas, Konstantinos Votis, Petros Daras, Dimitrios Tzovaras
2019 Sensors  
In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification  ...  Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks.  ...  Moreover, in [60, 61] , the authors proposed deep learning based methods for automatic target recognition based on Synthetic Aperture Radar (SAR) images.  ... 
doi:10.3390/s19224837 pmid:31698862 pmcid:PMC6891421 fatcat:rivnqa3uafdpnffieajljuc23a

2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31

2020 IEEE Transactions on Neural Networks and Learning Systems  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TNNLS Aug. 2020 3032-3046 Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search.  ...  ., +, TNNLS Nov. 2020 4892-4906 Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search.  ... 
doi:10.1109/tnnls.2020.3045307 fatcat:34qoykdtarewhdscxqj5jvovqy

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Hazim Shakhatreh, Ahmad H. Sawalmeh, Ala Al-Fuqaha, Zuochao Dou, Eyad Almaita, Issa Khalil, Noor Shamsiah Othman, Abdallah Khreishah, Mohsen Guizani
2019 IEEE Access  
The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery  ...  Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges.  ...  SEARCH AND RESCUE (SAR) In the wake of new scientific developments, speculations shot up with regard to the future potential of UAVs in the context of public and civil domains.  ... 
doi:10.1109/access.2019.2909530 fatcat:xgknpyuqazhpvferjkkdohxmtu

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

Dario Cazzato, Claudio Cimarelli, Jose Luis Sanchez-Lopez, Holger Voos, Marco Leo
2020 Journal of Imaging  
of such solutions for operations of the UAV.  ...  The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields.  ...  In the next few years, it is possible to expect the arising of deep neural networks that take multimodal input data and specifically designed for the UAV.  ... 
doi:10.3390/jimaging6080078 pmid:34460693 pmcid:PMC8321148 fatcat:ds4kpheadvg6xp2fambrp6nffq

RadarConf21 2021 Blank Page

2021 2021 IEEE Radar Conference (RadarConf21)  
Synthesize data to train Deep Learning and Machine Learning networks for a range of radar and wireless communications systems 4. Explore radar signals in the spectral and time-frequency domains 5.  ...  performance computing for SAR based automatic target recognition (ATR).  ...  More recently, he has developed deep learning neural networks for SIGINT and ELINT applications. He is the co-author of Non-Line-of-Sight Radar (Artech House).  ... 
doi:10.1109/radarconf2147009.2021.9454970 fatcat:gf7lve4dirh65jmkz7o4gzi754

Drone-View Building Identification by Cross-View Visual Learning and Relative Spatial Estimation

Chun-Wei Chen, Yin-Hsi Kuo, Tang Lee, Cheng-Han Lee, Winston Hsu
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
new drone-view datasets for the task.  ...  Our method outperforms triplet neural network by 0.12 mAP. (i.e., 22.9 to 35.0,  ...  Experiments show that our methods (CVDT+D a +D x +D y ) significantly improve the retrieval accuracy over triplet neural network (e.g., 22.9 to 35.0, +53% in LA).  ... 
doi:10.1109/cvprw.2018.00197 dblp:conf/cvpr/ChenKLLH18 fatcat:53ucxjrjxnfubiiyxen76itxy4
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