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Dapeng Chen
Dapeng Chen
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Year
Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification
Y Ge, D Chen, H Li
arXiv preprint arXiv:2001.01526, 2020
7422020
Self-paced contrastive learning with hybrid memory for domain adaptive object re-id
Y Ge, F Zhu, D Chen, RHL Zhao
Advances in Neural Information Processing Systems 33, 11309-11321, 2020
6372020
Similarity learning with spatial constraints for person re-identification
D Chen, Z Yuan, B Chen, N Zheng
Proceedings of the IEEE conference on computer vision and pattern …, 2016
3922016
Person re-identification with deep similarity-guided graph neural network
Y Shen, H Li, S Yi, D Chen, X Wang
Proceedings of the European conference on computer vision (ECCV), 486-504, 2018
3712018
Group Consistent Similarity Learning via Deep CRFs for Person Re-Identification
D Chen, D Xu, H Li, N Sebe, X Wang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
2692018
Similarity learning on an explicit polynomial kernel feature map for person re-identification
D Chen, Z Yuan, G Hua, N Zheng, J Wang
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015
2562015
Video Person Re-identification with Competitive Snippet-similarity Aggregation and Co-attentive Snippet Embedding
D Chen, H Li, T Xiao, S Yi, X Wang
IEEE Conference on Computer Vision and Pattern Recognition, 2018
2502018
The thermal infrared visual object tracking VOT-TIR2015 challenge results
M Felsberg, A Berg, G Hager, J Ahlberg, M Kristan, J Matas, A Leonardis, ...
Proceedings of the IEEE international conference on computer vision …, 2015
1932015
Deep group-shuffling random walk for person re-identification
Y Shen, H Li, T Xiao, S Yi, D Chen, X Wang
Proceedings of the IEEE conference on computer vision and pattern …, 2018
1762018
The visual object tracking vot2016 challenge results
G Roffo, S Melzi
Computer Vision--ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016
169*2016
Improving deep visual representation for person re-identification by global and local image-language association
D Chen, H Li, X Liu, Y Shen, J Shao, Z Yuan, X Wang
Proceedings of the European conference on computer vision (ECCV), 54-70, 2018
1662018
Show, tell and discriminate: Image captioning by self-retrieval with partially labeled data
X Liu, H Li, J Shao, D Chen, X Wang
Proceedings of the European conference on computer vision (ECCV), 338-354, 2018
1632018
I can find you! boundary-guided separated attention network for camouflaged object detection
H Zhu, P Li, H Xie, X Yan, D Liang, D Chen, M Wei, J Qin
Proceedings of the AAAI conference on artificial intelligence 36 (3), 3608-3616, 2022
1612022
Learning to cluster faces on an affinity graph
L Yang, X Zhan, D Chen, J Yan, CC Loy, D Lin
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
1532019
Learning to cluster faces via confidence and connectivity estimation
L Yang, D Chen, X Zhan, R Zhao, CC Loy, D Lin
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
1142020
Online pseudo label generation by hierarchical cluster dynamics for adaptive person re-identification
Y Zheng, S Tang, G Teng, Y Ge, K Liu, J Qin, D Qi, D Chen
Proceedings of the IEEE/CVF international conference on computer vision …, 2021
1082021
A survey on hallucination in large vision-language models
H Liu, W Xue, Y Chen, D Chen, X Zhao, K Wang, L Hou, R Li, W Peng
arXiv preprint arXiv:2402.00253, 2024
1002024
Cocas: A large-scale clothes changing person dataset for re-identification
S Yu, S Li, D Chen, R Zhao, J Yan, Y Qiao
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020
1002020
Complementary relation contrastive distillation
J Zhu, S Tang, D Chen, S Yu, Y Liu, M Rong, A Yang, X Wang
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021
992021
Adapting object detectors with conditional domain normalization
P Su, K Wang, X Zeng, S Tang, D Chen, D Qiu, X Wang
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
912020
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