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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Martínez-Tapia, Áulide Corona-Aquino, Samuel Triana-Arango, Freiman You, Chenglong Jin, Rui-Bo Magaña-Loaiza, Omar S. Dong, Shi-Hai U'Ren, Alfred B. de León-Montiel, Roberto J. Instituto de Ciencias Nucleares Universidad Nacional Autónoma de México Apartado Postal 70-543 Cd. Mx.04510 Mexico Centro de Investigaciones en Optica AC Apartado Postal 37150 Gto Leon Mexico Quantum Photonics Laboratory Department of Physics & Astronomy Louisiana State University Baton RougeLA70803 United States Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China Research Center for Quantum Physics Huzhou University Huzhou313000 China Laboratorio de Ciencias de la Informacion Cuantica CIC Instituto Politecnico Nacional UPALM CDMXC.P 07700 Mexico

We demonstrate that a two-photon, N00N-state interferometer is insensitive to linear (single-photon) losses, and thus capable to unequivocally certify entangled two-photon absorption (eTPA) in arbitrary-sample transmi... 详细信息

ISBN: (纸本)9781957171258

关键词： Photons

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Double Backpropagation for Training Autoencoders against Adversarial Attack

arXiv

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arXiv 2020年

作者： Sun, Chengjin Chen, Sizhe Huang, Xiaolin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China

Deep learning, as widely known, is vulnerable to adversarial samples. This paper focuses on the adversarial attack on autoencoders. Safety of the autoencoders (AEs) is important because they are widely used as a compression scheme for data storage and transmission, however, the current autoencoders are easily attacked, i.e., one can slightly modify an input but has totally different codes. The vulnerability is rooted the sensitivity of the autoencoders and to enhance the robustness, we propose to adopt double backpropagation (DBP) to secure autoencoder such as VAE and DRAW. We restrict the gradient from the reconstruction image to the original one so that the autoencoder is not sensitive to trivial perturbation produced by the adversarial attack. After smoothing the gradient by DBP, we further smooth the label by Gaussian Mixture Model (GMM), aiming for accurate and robust classification. We demonstrate in MNIST, CelebA, SVHN that our method leads to a robust autoencoder resistant to attack and a robust classifier able for image transition and immune to adversarial attack if combined with GMM. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

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Attention-Guided Multi-scale Interaction Network for Face Super-Resolution

arXiv

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arXiv 2024年

作者： Wan, Xujie Li, Wenjie Gao, Guangwei Lu, Huimin Yang, Jian Lin, Chia-Wen The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100080 China The School of Automation Southeast University Nanjing210096 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan

Recently, CNN and Transformer hybrid networks demonstrated excellent performance in face super-resolution (FSR) tasks. Since numerous features at different scales in hybrid networks, how to fuse these multi-scale features and promote their complementarity is crucial for enhancing FSR. However, existing hybrid network-based FSR methods ignore this, only simply combining the Transformer and CNN. To address this issue, we propose an attention-guided Multi-scale interaction network (AMINet), which contains local and global feature interactions as well as encoder-decoder phases feature interactions. Specifically, we propose a Local and Global Feature Interaction Module (LGFI) to promote fusions of global features and different receptive fields' local features extracted by our Residual Depth Feature Extraction Module (RDFE). Additionally, we propose a Selective Kernel Attention Fusion Module (SKAF) to adaptively select fusions of different features within LGFI and encoder-decoder phases. Our above design allows the free flow of multi-scale features from within modules and between encoder and decoder, which can promote the complementarity of different scale features to enhance FSR. Comprehensive experiments confirm that our method consistently performs well with less computational consumption and faster inference. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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One-shot distributed algorithm for PCA with RBF kernels

arXiv

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arXiv 2020年

作者： He, Fan Lv, Kexin Yang, Jie Huang, Xiaolin The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China The MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China

This letter proposes a one-shot algorithm for feature-distributed kernel PCA. Our algorithm is inspired by the dual relationship between sample-distributed and feature-distributed scenario. This interesting relationship makes it possible to establish distributed kernel PCA for feature-distributed cases from ideas in distributed PCA in sample-distributed scenario. In theoretical part, we analyze the approximation error for both linear and RBF kernels. The result suggests that when eigenvalues decay fast, the proposed algorithm gives high quality results with low communication cost. This result is also verified by numerical experiments, showing the effectiveness of our algorithm in practice. Copyright © 2020, The Authors. All rights reserved.

关键词： Principal component analysis

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Based on improved joint detection and tracking of UAV for multi-target detection of livestock

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Heliyon 2024年第19期10卷 e38316页

作者： Shen, Peng Wang, Fulong Luo, Wei Zhao, Yongxiang Li, Lin Zhang, Guoqing Zhu, Yuchen North China Institute of Aerospace Engineering Langfang 065000 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province Langfang 065000 China Fujian Provincial Key Laboratory of Water Cycling and Eco-Geological Processes Xiamen 361021 China Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological Science Shijiazhuang 050061 China

In agriculture, specifically livestock monitoring, drones' ability to track multiple targets is essential for advancing the field. However, limited computing resources and unpredictable drone movements often cause issues like ambiguous video frames, object obstructions, and size deviations. These inconsistencies reduce tracking accuracy, making traditional algorithms inadequate for handling drone footage. This study introduces an enhanced deep learning-based multi-target drone tracker framework that enables real-time processing. The proposed method combines object detection and tracking by leveraging consecutive frame pairs to extract and share features, enhancing computational efficiency. It employs diverse loss functions to address class and sample distribution imbalances and includes a composite deblurring module to enhance detection accuracy. Object association utilizes a dual regress bounding box technique, aiding in object identification verification and predictive motion. Live tracking is achieved by predicting object locations in subsequent frames, enabling real-time tracking. Evaluation against leading benchmarks shows that the system improves precision and speed, achieving a 4.3 % increase in Multi-Object Tracking Accuracy (MOTA) and a 7.7 % boost in F1 score. © 2024 The Authors

关键词： Animal monitoring Multi-object detection UAV

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An Enhanced Coarse-to-Fine Framework for the Segmentation of Clinical Target Volume 23rd

An Enhanced Coarse-to-Fine Framework for the Segmentation of...

引用

Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Qian, Dahong Liu, Weiping Li, Hui Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Algorithm and Research Shanghai Aitrox Technology Co. Ltd. Shanghai China National Key Laboratory of Science and Technology on Nano/Micro Fabrication Key Laboratory for Thin Film and Micro Fabrication of the Ministry of Education Institute of Micro-Nano Science and Technology Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783030718268

In radiation therapy, obtaining accurate boundary of the clinical target volume (CTV) is the vital step to decrease the risk of treatment failures. However, it is a time-consuming and laborious task to obtain the delineation by hand. Therefore, an automatic algorithm is urgently needed to realize accurate segmentation. In this paper, we propose an enhanced coarse-to-fine frameworkto automatically fuse the information of CT, T1 and T2 images to get the target region. This framework includes a coarse-segmentation stage to identify the region of interest (ROI) of targets and a fine-segmentation stage to iteratively refine the segmentation. In the coarse-segmentation stage, the F-loss is proposed to keep the high recall rate of the ROI. In the fine segmentation, the ROI of target will be first cropped according to the ROI obtained by coarse-segmentation and be fed into a 3D-Unet to get the initial results. Then, the prediction and medium features will be set as additional information for the next one network to refine the results. When evaluated on the validation dataset of challenge of Anatomical Brain Barriers to Cancer Spread (ABCs), our method won the 3th place in the public leaderboard. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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An Hybrid Attention-Based System for the Prediction of Facial Attributes 4th

An Hybrid Attention-Based System for the Prediction of Fac...

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4th International Workshop on Brain-Inspired Computing, BrainComp 2019

作者： Khellat-Kihel, Souad Sun, Zhenan Tistarelli, Massimo Computer Vision Laboratory University of Sassari Viale Italia 39 Sassari07100 Italy Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Room 1605 Intelligence Bulding 95 Zhongguancun East Road Beijing100190 China Computer Vision Laboratory Department of Biomedical Sciences and Information Technology University of Sassari Viale S. Pietro 43/b Sassari07100 Italy

ISBN: (纸本)9783030824266

Recent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood process in analyzing a scene or an object, such as a face. The direction of the eye gaze is repeatedly directed, through purposively planned saccadic movements, towards salient regions to capture several details. In this paper we propose to capitalize on the knowledge of the saccadic human visual processes to design a system to predict facial attributes embedding a biologically-inspired network architecture, the HMAX. The architecture is tailored to predict attributes with different textural information and conveying different semantic meaning, such as attributes related and unrelated to the subject’s identity. Salient points on the face are extracted from the outputs of the S2 layer of the HMAX architecture and fed to a local texture characterization module based on LBP (Local Binary pattern). The resulting feature vector is used to perform a binary classification on a set of pre-defined visual attributes. The devised system allows to distill a very informative, yet robust, representation of the imaged faces, allowing to obtain high performance but with a much simpler architecture as compared to a deep convolutional neural network. Several experiments performed on publicly available, challenging, large datasets demonstrate the validity of the proposed approach. © 2021, The Author(s).

关键词： Network architecture

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A correction method for aero-optics thermal radiation effects based on gradient distribution and dark channel

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Optoelectronics Letters 2019年第5期15卷 374-380页

作者： HONG Han-yu SHI Yu ZHANG Tian-xu LIU Zhao Hubei Key Laboratory of Optical Information and Pattern Recognition Hubei Research Centre of Video Image and High Definition Projection School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan 430074 China National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Automation Huazhong University of Science and Technology Wuhan 430074 China

In this paper, a new algorithm is proposed to remove the effects of aerodynamic optical thermal radiation from a single infrared image. In this method, the joint probability model of gradient distribution is introduced by studying the "global smoothing and local fluctuation" characteristics of the bias field. A prior L0 norm of dark channel is introduced to constrain the latent clear image. Finally, the split Bregman method is used to solve the optimization problem. The effectiveness of the proposed method is verified by a series of experiments, and the results are compared with the results of the existing methods for the correction of thermal radiation effects.

关键词： aero-optics thermal radiation effects gradient distribution dark channel

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Sparse slow feature analysis for enhanced control monitoring and fault isolation 1

Sparse slow feature analysis for enhanced control monitoring...

引用

1st International Conference on Industrial Artificial Intelligence, IAI 2019

作者： Shang, Chao Huang, Xiaolin Yang, Fan Huang, Dexian Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200400 China

ISBN: (纸本)9781728135939

This paper presents sparse slow feature analysis (SFA) for efficient process monitoring and fault isolation, which is a new latent variable model for time series data. We first recast sparse SFA in terms of a novel regression-Type problem, and further incorporate l-{1}-norm penalty into the objective in order to promote sparsity. To solve the induced nonconvex optimization problem, a tailored iterative algorithm is developed. With the sparse representation, process variables with insignificant contributions can be completely omitted, and each latent variable relates only to a fraction of crucial process variables in comparison with the generic SFA. A new process monitoring and fault isolation approach is developed based on the sparse SFA, which results in improved monitoring performance, easy-To-interpret diagnostics, and meaningful process knowledge discovery. Case studies on the Tennessee Eastman process are carried out to address the applicability of the proposed approach. © 2019 IEEE.

关键词： Process monitoring

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VSR++: Improving Visual Semantic Reasoning for Fine-Grained Image-Text Matching

VSR++: Improving Visual Semantic Reasoning for Fine-Grained ...

引用

International Conference on pattern recognition

作者： Hui Yuan Yan Huang Dongbo Zhang Zerui Chen Wenlong Cheng Liang Wang Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) Institute of Automation Chinese Academy of Sciences (CASIA) The College of Automation and Electronic Information Xiangtan University Xiangtan China Chinese Academy of Sciences Artificial Intelligence Research (CAS-AIR)

Image-text matching has made great progresses recently, but there still remains challenges in fine-grained matching. To deal with this problem, we propose an Improved Visual Semantic Reasoning model (VSR++), which jointly models 1) global alignment between images and texts and 2) local correspondence between regions and words in a unified framework. To exploit their complementary advantages, we also develop a suitable learning strategy to balance their relative importance. As a result, our model can distinguish image regions and text words in a fine-grained level, and thus achieves the current state-of-the-art performance on two benchmark datasets.

关键词： Visualization Semantics Benchmark testing Cognition pattern matching

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