Image generation and constrained two-stage feature fusion for person re-identification
Applied Intelligence, ISSN: 1573-7497, Vol: 51, Issue: 11, Page: 7679-7689
2021
- 12Citations
- 6Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Generative adversarial network is widely used in person re-identification to expand data by generating auxiliary data. However, researchers all believe that using too much generated data in the training phase will reduce the accuracy of re-identification models. In this study, an improved generator and a constrained two-stage fusion network are proposed. A novel gesture discriminator embedded into the generator is used to calculate the completeness of skeleton pose images. The improved generator can make generated images more realistic, which would be conducive to feature extraction. The role of the constrained two-stage fusion network is to extract and utilize the real information of the generated images for person re-identification. Unlike previous studies, the fusion of shallow features is considered in this work. In detail, the proposed network has two branches based on the structure of ResNet50. One branch is for the fusion of images that are generated by the generated adversarial network, the other is applied to fuse the result of the first fusion and the original image. Experimental results show that our method outperforms most existing similar methods on Market-1501 and DukeMTMC-reID.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85102788371&origin=inward; http://dx.doi.org/10.1007/s10489-021-02271-z; https://link.springer.com/10.1007/s10489-021-02271-z; https://link.springer.com/content/pdf/10.1007/s10489-021-02271-z.pdf; https://link.springer.com/article/10.1007/s10489-021-02271-z/fulltext.html; https://dx.doi.org/10.1007/s10489-021-02271-z; https://link.springer.com/article/10.1007/s10489-021-02271-z
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know