NDLI: Enhanced multi-dataset transfer learning method for unsupervised person re-identification using co-training strategy

Content Provider	IET Digital Library
Author	Xian, Yuqiao Hu, Haifeng
Abstract	This study proposes progressive unsupervised co-learning for unsupervised person re-identification by introducing a co-training strategy in an iterative training process. The authors’ method adopts an iterative training process to improve transferred models by iterating among clustering, selection, exchange, and fine-tuning. To solve the problem of transferring representations learned from multiple source datasets, their method utilises multiple convolutional neural network (CNN) models trained on different labelled source datasets by feeding soft labels obtained by clustering on target dataset to each other. The enhanced model can learn more discriminative person representations than the single model trained on multiple datasets. Experimental results on two large-scale benchmark datasets (i.e. DukeMTMC-reID and Market-1501) demonstrate that their method can enhance transferred CNN models by using more source datasets and is competitive to the state-of-the-art methods.
Starting Page	1219
Ending Page	1227
Page Count	9
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 8, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5103
Journal	IET Computer Vision
Publisher Date	2018-09-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CNN Model Co-training Strategy Computer Vision And Image Processing Technique Data Handling Technique Discriminative Person Representations Enhanced Multidataset Transfer Learning Method Image Recognition Image Representation Interpolation And Function Approximation Iterative Method Iterative Training Process Labelled Source Datasets Large-scale Benchmark Datasets Multiple Convolutional Neural Network Model Multiple Source Datasets Neural Computing Technique Neural Nets Numerical Analysis Pattern Clustering Progressive Unsupervised Co-learning Single Model Soft Labels Target Dataset Clustering Transferred Model Unsupervised Learning Unsupervised Person Re-identification
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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