Joint Cuts and Matching of Partitions in One Graph

Yu, Tianshu; Yan, Junchi; Zhao, Jieyi; Li, Baoxin

doi:10.1109/cvpr.2018.00080

Cited by 13 publications

(6 citation statements)

References 31 publications

(37 reference statements)

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“…The experiments on three benchmark data sets show its advantages. In the future, we plan to apply the proposed method other applications, such as biometrics [20,27], network analysis [24], human pose estimation [11,10,9], mobile computing [16,8], mathematics [17,2,1,18], etc. The similar approach can also be adopted in other related fields such as systems [6,7,14], and system security [4,5,22].…”

Section: Resultsmentioning

confidence: 99%

Cross-Domain Attribute Representation Based on Convolutional Neural Network

Zhang

Liang

et al. 2018

Intelligent Computing Methodologies

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In the problem of domain transfer learning, we learn a model for the prediction in a target domain from the data of both some source domains and the target domain, where the target domain is in lack of labels while the source domain has sufficient labels. Besides the instances of the data, recently the attributes of data shared across domains are also explored and proven to be very helpful to leverage the information of different domains. In this paper, we propose a novel learning framework for domain-transfer learning based on both instances and attributes. We proposed to embed the attributes of different domains by a shared convolutional neural network (CNN), learn a domain-independent CNN model to represent the information shared by different domains by matching across domains, and a domain-specific CNN model to represent the information of each domain. The concatenation of the three CNN model outputs is used to predict the class label. An iterative algorithm based on gradient descent method is developed to learn the parameters of the model. The experiments over benchmark datasets show the advantage of the proposed model.

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Section: Resultsmentioning

confidence: 99%

Cross-Domain Attribute Representation Based on Convolutional Neural Network

Zhang

Liang

et al. 2018

Intelligent Computing Methodologies

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“…To address the influence of video compression on heart rate measurement, a two‐stage, end‐to‐end method, proposed by Yu et al, [ 73 ] was the first attempt to extract physiological signals from compressed videos. It consists of two parts, namely, the STVEN and rPPGNet, while the STVEN aims to enhance the video and the rPPGNet is designed for the heart rate signals recovery with a skin‐based module.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Zhang

et al. 2023

Advanced Intelligent Systems

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In recent years, optical imaging techniques have gained wide recognition for the measurement of vital signals, such as heart rate, respiratory rate, oxygen saturation, and blood pressure, which are crucial indicators for evaluating human health conditions in clinical examinations. There is a wide range of optical imaging methods for remote physiological signal monitoring, including RGB imaging, thermal imaging, hyperspectral imaging, depth imaging, and multimodal imaging, which provide spatial information compared to other noncontact measurement approaches, thereby allowing extensive applications in this area. In this survey, some fundamental knowledge about optical imaging methods for vital signal measurement is reviewed, including principles of various optical imaging techniques, processing methods for data analysis, discussion on advantages and disadvantages, application summary, and future prospects. This is a comprehensive overview of the noncontact physiological signal measurement of optical imaging approaches.

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“…For future work, to deal with the fundamental challenge for relatively small size training dataset problem and the varying drift of the objects for analysis in real-world visual audit setting, we plan to resort to image matching and registration [33], [34], as well as structure matching based approaches [34]- [36].…”

Section: Signature Action Positioningmentioning

confidence: 99%

Video Content Analysis for Compliance Audit in Finance and Security Industry

Liu

Tao³

et al. 2020

IEEE Access

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The quality and accessibility of modern financial service have been quickly and dramatically improved, which benefits from the fast development of information technology. It has also witnessed the trend for applying artificial intelligence related technology, especially machine learning to the finance and security industry ranging from face recognition to fraud detection. In particular, deep neural networks have proven to be far superior to traditional algorithms in various application scenarios of computer vision. In this paper, we propose a deep learning-based video analysis system for automated compliance audit in stock brokerage, which in general consists of five modules here: 1) Video tampering and integrity detection; 2) Objects of interest localization and association; 3) Analysis of presence and departure of personnel in a video; 4) Face image quality assessment; and 5) Signature action positioning. To the best of our knowledge, this is the first work that introduces remote automated compliance audit system for the dual-recorded video in finance and security industry. The experimental results suggest our system can identify most of the potential non-compliant videos and has greatly improved the working efficiency of the auditors and reduced human labor costs. The collected dataset in our experiment will be released with this paper.

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Joint Cuts and Matching of Partitions in One Graph

Cited by 13 publications

References 31 publications

Cross-Domain Attribute Representation Based on Convolutional Neural Network

Cross-Domain Attribute Representation Based on Convolutional Neural Network

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Video Content Analysis for Compliance Audit in Finance and Security Industry

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