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Energy Minimization Methods in Computer Vision and Pattern Recognition: Third International Workshop, EMMCVPR 2001, Sophia Antipolis France, September 3-5, 2001. Proceedings

2001 edition., 2001

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Energy Minimization Methods in Computer Vision and Pattern Recognition: Second International Workshop, EMMCVPR'99, York, UK, July 26-29, 1999, Proceedings

1, 1999

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Energy Minimization Methods in Computer Vision and Pattern Recognition: International Workshop EMMCVPR'97 Venice, Italy, May 21–23, 1997 Proceedings

1997

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Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey

Remote sensing (Basel, Switzerland), 2022-12, Vol.14 (23), p.6017

2022

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Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence

1, 2022

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Closing Editorial for Computer Vision and Pattern Recognition Based on Deep Learning

Applied sciences, 2024-05, Vol.14 (9), p.3660

Yuan, Hui

2024

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NetVLAD: CNN Architecture for Weakly Supervised Place Recognition

IEEE transactions on pattern analysis and machine intelligence, 2018-06, Vol.40 (6), p.1437-1451

2018

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IEEE_Xplore

An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition

IEEE transactions on pattern analysis and machine intelligence, 2017-11, Vol.39 (11), p.2298-2304

2017

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Attention mechanisms in computer vision: A survey

Computational visual media (Beijing), 2022-09, Vol.8 (3), p.331-368

2022

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Long-Term Temporal Convolutions for Action Recognition

IEEE transactions on pattern analysis and machine intelligence, 2018-06, Vol.40 (6), p.1510-1517

2018

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IEEE_Xplore

A Continual Learning Survey: Defying Forgetting in Classification Tasks

IEEE transactions on pattern analysis and machine intelligence, 2022-07, Vol.44 (7), p.3366-3385

2022

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IEEE_Xplore

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers

IEEE transactions on visualization and computer graphics, 2019-08, Vol.25 (8), p.2674-2693

2019

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IEEE_Xplore

A Style-Based Generator Architecture for Generative Adversarial Networks

IEEE transactions on pattern analysis and machine intelligence, 2021-12, Vol.43 (12), p.4217-4228

2021

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IEEE_Xplore

Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition

IEEE transactions on pattern analysis and machine intelligence, 2016-08, Vol.38 (8), p.1583-1597

2016

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Adaptive Batch Normalization for practical domain adaptation

Pattern recognition, 2018-08, Vol.80, p.109-117

2018

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Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition

IEEE transactions on pattern analysis and machine intelligence, 2018-06, Vol.40 (6), p.1309-1322

2018

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PCT: Point cloud transformer

Computational visual media (Beijing), 2021-06, Vol.7 (2), p.187-199

2021

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Large scale deep learning for computer aided detection of mammographic lesions

Medical image analysis, 2017-01, Vol.35, p.303-312

2017

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Learning Two-Branch Neural Networks for Image-Text Matching Tasks

IEEE transactions on pattern analysis and machine intelligence, 2019-02, Vol.41 (2), p.394-407

2019

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IEEE_Xplore

Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition

IEEE transactions on pattern analysis and machine intelligence, 2018-04, Vol.40 (4), p.1002-1014

2018

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IEEE_Xplore

Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images

Pattern recognition letters, 2020-01, Vol.129, p.181-189

2020

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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

IEEE transactions on pattern analysis and machine intelligence, 2017-12, Vol.39 (12), p.2481-2495

2017

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COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

Pattern recognition letters, 2020-10, Vol.138, p.638-643

2020

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An Introduction to Machine Learning

Kubat, Miroslav

3rd ed. 2021., 2017

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Using filter banks in Convolutional Neural Networks for texture classification

Pattern recognition letters, 2016-12, Vol.84, p.63-69

2016

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Energy Minimization Methods in Computer Vision and Pattern Recognition: Third International Workshop, EMMCVPR 2001, Sophia Antipolis France, September 3-5, 2001. Proceedings

Energy Minimization Methods in Computer Vision and Pattern Recognition: Second International Workshop, EMMCVPR'99, York, UK, July 26-29, 1999, Proceedings

Energy Minimization Methods in Computer Vision and Pattern Recognition: International Workshop EMMCVPR'97 Venice, Italy, May 21–23, 1997 Proceedings

Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey

Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence

Closing Editorial for Computer Vision and Pattern Recognition Based on Deep Learning

NetVLAD: CNN Architecture for Weakly Supervised Place Recognition

An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition

Attention mechanisms in computer vision: A survey

Long-Term Temporal Convolutions for Action Recognition

A Continual Learning Survey: Defying Forgetting in Classification Tasks

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers

A Style-Based Generator Architecture for Generative Adversarial Networks

Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition

Adaptive Batch Normalization for practical domain adaptation

Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition

PCT: Point cloud transformer

Large scale deep learning for computer aided detection of mammographic lesions

Learning Two-Branch Neural Networks for Image-Text Matching Tasks

Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition

Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

An Introduction to Machine Learning

Using filter banks in Convolutional Neural Networks for texture classification

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