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Hierarchical Relational Networks for Group Activity Recognition and Retrieval

Computer Vision – ECCV 2018, p.742-758

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A Discriminative Latent Model of Object Classes and Attributes

Computer Vision – ECCV 2010, p.155-168

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A Hierarchical Deep Temporal Model for Group Activity Recognition

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, p.1971-1980

2016

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Action recognition by learning mid-level motion features

2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008, p.1-8

2008

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Deep Neural Network Compression by In-Parallel Pruning-Quantization

IEEE transactions on pattern analysis and machine intelligence, 2020-03, Vol.42 (3), p.568-579

2020

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Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets

PloS one, 2017-07, Vol.12 (7), p.e0180318-e0180318

2017

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Visual recognition by counting instances: A multi-instance cardinality potential kernel

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, p.2596-2605

2015

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CLIP-Q: Deep Network Compression Learning by In-parallel Pruning-Quantization

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, p.7873-7882

2018

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Similarity Constrained Latent Support Vector Machine: An Application to Weakly Supervised Action Classification

Computer Vision – ECCV 2012, p.55-68

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Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation

Computer Vision – ECCV 2008, p.710-724

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Learning a Deep ConvNet for Multi-Label Classification With Partial Labels

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, p.647-657

2019

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Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV

1st Edition 2019, 2019

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Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part VI

1, 2019

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Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part III

1, 2019

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Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part I

1, 2019

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Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part II

1, 2019

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Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part V

1, 2019

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Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks

IEEE transactions on pattern analysis and machine intelligence, 2017-09, Vol.39 (9), p.1839-1852

2017

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Human Action Recognition by Semilatent Topic Models

IEEE transactions on pattern analysis and machine intelligence, 2009-10, Vol.31 (10), p.1762-1774

2009

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Hidden Part Models for Human Action Recognition: Probabilistic versus Max Margin

IEEE transactions on pattern analysis and machine intelligence, 2011-07, Vol.33 (7), p.1310-1323

2011

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Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos

International journal of computer vision, 2018-04, Vol.126 (2-4), p.375-389

2018

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End-to-End Learning of Action Detection from Frame Glimpses in Videos

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, p.2678-2687

2016

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Handling Uncertain Tags in Visual Recognition

2013 IEEE International Conference on Computer Vision, 2013, p.737-744

2013

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"You two! Take off!": Creating, modifying and commanding groups of robots using face engagement and indirect speech in voice commands

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, p.137-142

2013

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Discriminative Latent Models for Recognizing Contextual Group Activities

IEEE transactions on pattern analysis and machine intelligence, 2012-08, Vol.34 (8), p.1549-1562

2012

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Hierarchical Relational Networks for Group Activity Recognition and Retrieval

A Discriminative Latent Model of Object Classes and Attributes

A Hierarchical Deep Temporal Model for Group Activity Recognition

Action recognition by learning mid-level motion features

Deep Neural Network Compression by In-Parallel Pruning-Quantization

Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets

Visual recognition by counting instances: A multi-instance cardinality potential kernel

CLIP-Q: Deep Network Compression Learning by In-parallel Pruning-Quantization

Similarity Constrained Latent Support Vector Machine: An Application to Weakly Supervised Action Classification

Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation

Learning a Deep ConvNet for Multi-Label Classification With Partial Labels

Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part IV

Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part VI

Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part III

Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part I

Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part II

Computer Vision - ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2-6, 2018, Revised Selected Papers, Part V

Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks

Human Action Recognition by Semilatent Topic Models

Hidden Part Models for Human Action Recognition: Probabilistic versus Max Margin

Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos

End-to-End Learning of Action Detection from Frame Glimpses in Videos

Handling Uncertain Tags in Visual Recognition

"You two! Take off!": Creating, modifying and commanding groups of robots using face engagement and indirect speech in voice commands

Discriminative Latent Models for Recognizing Contextual Group Activities

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