Results 241 to 250 of about 649,643 (307)

Generative super-resolution of turbulent flows via stochastic interpolants. [PDF]

Sci Rep
Schiødt M, Mücke NT, Velte CM.
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Dance with Self-Attention: A New Look of Conditional Random Fields on Anomaly Detection in Videos

IEEE International Conference on Computer Vision, 2021
This paper proposes a novel weakly supervised approach for anomaly detection, which begins with a relation-aware feature extractor to capture the multi-scale convolutional neural network (CNN) features from a video.
Didik Purwanto, Yie-Tarng Chen, Wen Fang
semanticscholar   +1 more source

A Novel Sleep Stage Contextual Refinement Algorithm Leveraging Conditional Random Fields

IEEE Transactions on Instrumentation and Measurement, 2022
Automatic sleep stage classification has gained much attention in recent researches. Various classification algorithms have been proposed for automatic sleep staging, including deep neural networks and traditional machine learning models.
Bufang Yang, Wenxuan Wu, Yitian Liu, Hongxing Liu   +3 more
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Hidden Conditional Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state Conditional Random Field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time.
Ariadna, Quattoni, Sybor, Wang, Louis-Philippe, Morency, Michael, Collins, Trevor, Darrell   +4 more
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Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016
Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems.
P. Christ, Mohamed Ezzeldin A. Elshaer, Florian Ettlinger, S. Tatavarty, Marc Bickel, Patrick Bilic, M. Rempfler, M. Armbruster, Felix O. Hofmann, M. D’Anastasi, W. Sommer, Seyed-Ahmad Ahmadi, Bjoern H Menze   +12 more
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Efficient robust conditional random fields

IEEE Transactions on Image Processing, 2015
Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features ...
Dongjin, Song, Wei, Liu, Tianyi, Zhou, Dacheng, Tao, David A, Meyer   +4 more
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Kernel conditional random fields

Twenty-first international conference on Machine learning - ICML '04, 2004
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs.
John Lafferty, Xiaojin Zhu, Yan Liu
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MVSCRF: Learning Multi-View Stereo With Conditional Random Fields

IEEE International Conference on Computer Vision, 2019
We present a deep-learning architecture for multi-view stereo with conditional random fields (MVSCRF). Given an arbitrary number of input images, we first use a U-shape neural network to extract deep features incorporating both global and local ...
Youze Xue, Jiansheng Chen, Weitao Wan, Yiqing Huang, Cheng Yu, Tianpeng Li, Jiayu Bao   +6 more
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