Junyu Gao's Publication

Crowd Analysis

Crowd Counting, Localization

Pixel-wise Crowd Understanding via Synthetic Data
Q. Wang (supervisor), J. Gao, W. Lin, and Y. Yuan
International Journal of Computer Vision, 2020
[Paper] | [Bibtex] | [Homepage (Dataset/Code)]

@article{wang2020pixel,
	title={Pixel-Wise Crowd Understanding via Synthetic Data},
	author={Wang, Qi and Gao, Junyu and Lin, Wei and Yuan, Yuan},
	journal={International Journal of Computer Vision},
	doi={10.1007/s11263-020-01365-4},
	year={2020},
	publisher={Springer}
	}

NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
Q. Wang (supervisor), J. Gao, W. Lin, and X. Li
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
[Paper] | [Bibtex] | [Homepage (Dataset/Code/Benchmark)]

@article{gao2020nwpu,
	title={NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization},
	author={Wang, Qi and Gao, Junyu and Lin, Wei and Li, Xuelong},
	journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
	doi={10.1109/TPAMI.2020.3013269},
	year={2020}
	}

Feature-aware Adaptation and Structured Density Alignment for Crowd Counting in Video Surveillance
J. Gao, Q. Wang, and Y. Yuan
IEEE Transactions on Cybernetics, 2020
[Paper] | [Bibtex]

@article{gao2020feature,
	title={Feature-Aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance},
	author={Gao, Junyu and Wang, Qi and others},
	journal={IEEE Transactions on Cybernetics},
	doi={10.1109/TCYB.2020.3034316},
	year={2020},
	}

Density-aware Curriculum Learning for Crowd Counting
Q. Wang, W. Lin, J. Gao and X. Li
IEEE Transactions on Cybernetics, 2020
[Paper] | [Code]

Focus on Semantic Consistency for Cross-Domain Crowd Understanding
T. Han, J. Gao, W. Lin, Y. Yuan, and Q. Wang
Proc. International Conference on Acoustic, Speech, and Signal Processing, 2020
[Paper] | [Bibtex] | [Code (coming soon)]

@INPROCEEDINGS{han2020focus,
	author={Han,Tao and  Gao, Junyu and Yuan, Yuan and Wang, Qi and},
	booktitle={Proc. IEEE International Conference on Acoustics, Speech and Signal Processing}, 
	title={Focus on Semantic Consistency for Cross-Domain Crowd Understanding}, 
	year={2020},
	pages={1848-1852}
}

Learning from Synthetic Data for Crowd Counting in the Wild
Q. Wang (supervisor), J. Gao, W. Lin, and Y. Yuan
Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2019
[Paper] | [Bibtex] | [Homepage (Dataset/Code)]

@inproceedings{gao2019learning,
	title={Learning from Synthetic Data for Crowd Counting in the Wild},
	author={Wang, Qi and Gao, Junyu and Lin, Wei and Yuan, Yuan},
	booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition},
	pages={8198--8207},
	year={2019}
	}

PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
J. Gao, Q. Wang, and X. Li
IEEE Transactions on Circuits and Systems for Video Technology, 2019
[Paper] | [Bibtex] | [Code]

@ARTICLE{gao2020pcc, 
	author={Gao, Junyu and Wang, Qi and Li, Xuelong}, 
	journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
	title={PCC Net: Perspective Crowd Counting via Spatial Convolutional Network}, 
	year={2020}, 
	volume={30}, 
	number={10}, 
	pages={3486-3498}, 
	}

SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting
J. Gao, Q. Wang, and Y. Yuan
Neurocomputing, 2019
[Paper] | [Bibtex] | [Code]

@article{gao2019scar,
	title={SCAR: Spatial-/channel-wise attention regression networks for crowd counting},
	author={Gao, Junyu and Wang, Qi and Yuan, Yuan},
	journal={Neurocomputing},
	volume={363},
	pages={1--8},
	year={2019}
	}

Semantic Segmentation

Street Scene Labeling, Road Segmentation, Lane Segmentation

Multi-task Attention Network for Lane Detection and Fitting
Q. Wang, T. Han, Z. Qin, J. Gao, and X. Li
IEEE Transactions on Neural Networks and Learning Systems, 2020
[Paper]

Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes
Q. Wang (supervisor), J. Gao, and X. Li
IEEE Transactions on Image Processing, 2019
[Paper] | [Bibtex] | [Code]

@ARTICLE{gao2019weakly, 
	author={Wang, Qi and Gao, Junyu and Li, Xuelong}, 
	journal={IEEE Transactions on Image Processing}, 
	title={Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes}, 
	year={2019}, 
	volume={28}, 
	number={9}, 
	pages={4376-4386}, 
	}

Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection
J. Gao, Q. Wang, and Y. Yuan
Proc. IEEE International Conference on Robotics and Automation, 2017
IEEE Transactions on Intelligent Transportation Systems, 2018
[Conferencr Paper] [Journal Paper] | [Bibtex]

@inproceedings{gao2017embedding,
	title={Embedding structured contour and location prior in siamesed fully convolutional networks for road detection},
	author={Gao, Junyu and Wang, Qi and Yuan, Yuan},
	booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
	pages={219--224},
	year={2017}
	}
@ARTICLE{gao2018journal_embedding, 
	author={Wang, Qi and Gao, Junyu and Yuan. Yuan}, 
	journal={IEEE Transactions on Intelligent Transportation Systems}, 
	title={Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection}, 
	year={2018}, 
	volume={19}, 
	number={1}, 
	pages={230-241}
	}

A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling
Q. Wang (supervisor), J. Gao, and Y. Yuan
IEEE Transactions on Intelligent Transportation Systems, 2018
[Paper] | [Bibtex]

@ARTICLE{gao2018joint, 
	author={Wang, Qi and Gao, Junyu and Yuan. Yuan}, 
	journal={IEEE Transactions on Intelligent Transportation Systems}, 
	title={A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling}, 
	year={2018}, 
	volume={19}, 
	number={5}, 
	pages={1457-1470}
	}

Image Classification

Image Classification, Semi-Supervised Learning

Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
T. Han#, J. Gao#, Y. Yuan, and Q. Wang (Co-first Author)
Proc. Conference on Neural Information Processing Systems, 2020
[Paper] | [Bibtex] | [Code]

@inproceedings{han2020unsupervised,
	title={Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning},
	author={Han, Tao and Gao, Junyu and Yuan, Yuan and Wang, Qi},
	journal={Advances in Neural Information Processing Systems},
	volume={33},
	year={2020}
	}

Image Enchancement

Super-Resolution, Image Synthesis

Pixel-Level Self-Paced Learning for Super-Resolution
W. Lin, J. Gao, Q. Wang, and Y. Yuan
Proc. International Conference on Acoustic, Speech, and Signal Processing, 2020
[Paper] | [Bibtex] | [Code]

@INPROCEEDINGS{lin2020pixel,
	author={Lin, Wei and Gao, Junyu and  Wang, Qi and Li Xuelong},
	booktitle={Proc. IEEE International Conference on Acoustics, Speech and Signal Processing}, 
	title={Pixel-Level Self-Paced Learning For Super-Resolution}, 
	year={2020},
	pages={2538-2542}
}

ArXiv

Pre-print, Technical Report

Learning Independent Instance Maps for Crowd Localization
J. Gao, T. Han, Q. Wang, and Y. Yuan
arXiv preprint arXiv:2012.04164, 2020
[Paper] | [Bibtex] | [Code]

@article{gao2020learning,
	title={Learning Independent Instance Maps for Crowd Localization},
	author={Gao, Junyu and Han, Tao and Yuan, Yuan and Wang, Qi},
	journal={arXiv preprint arXiv:2012.04164},
	year={2020}
	}

Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting
Q. Wang, T. Han, J. Gao, and Y. Yuan
arXiv preprint arXiv:2004.02133, 2020
[Paper] | [Bibtex] | [Code (coming soon)]

CNN-based Density Estimation and Crowd Counting: A Survey
G. Gao, J. Gao, Q. Liu, Q. Wang, and Y. Wang
arXiv preprint arXiv:2003.12783, 2020
[Paper] | [Bibtex] | [Homepage (Code/Materials)]

Domain-adaptive Crowd Counting via Inter-domain Features Segregation and Gaussian-prior Reconstruction
J. Gao, T. Han, Q. Wang, and Y. Yuan
arXiv preprint arXiv:1912.03677, 2019
[Paper] | [Bibtex] | [Code (coming soon)]

C^3 Framework: An Open-source PyTorch Code for Crowd Counting
J. Gao, W. Lin, B. Zhao, D. Wang, C. Gao, and J. Wen
arXiv preprint arXiv:1907.02724, 2019
[Paper] | [Bibtex] | [Code]

@article{gao2019c,
	title={C$^3$ Framework: An Open-source PyTorch Code for Crowd Counting},
	author={Gao, Junyu and Lin, Wei and Zhao, Bin and Wang, Dong and Gao, Chenyu and Wen, Jun},
	journal={arXiv preprint arXiv:1907.02724},
	year={2019}
	}

Publication

Crowd Analysis

Semantic Segmentation

Imgae Classification

Image Enahncement

ArXiv

Crowd Analysis

Crowd Counting, Localization

Semantic Segmentation

Street Scene Labeling, Road Segmentation, Lane Segmentation

Image Classification

Image Classification, Semi-Supervised Learning

Image Enchancement

Super-Resolution, Image Synthesis

ArXiv

Pre-print, Technical Report