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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.3029415
|2 doi
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|a pubmed24n1303.xml
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|a eng
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| 100 |
1 |
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|a Fu, Dengpan
|e verfasserin
|4 aut
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| 245 |
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|a Improving Person Re-identification with Iterative Impression Aggregation
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|c 2020
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 22.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Our impression about one person often updates after we see more aspects of him/her and this process keeps iterating given more meetings. We formulate such an intuition into the problem of person re-identification (re-ID), where the representation of a query (probe) image is iteratively updated with new information from the candidates in the gallery. Specifically, we propose a simple attentional aggregation formulation to instantiate this idea and showcase that such a pipeline achieves competitive performance on standard benchmarks including CUHK03, Market-1501 and DukeMTMC. Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods. Another advantage of this proposal is its flexibility to incorporate different representations and similarity metrics. By utilizing stronger representations and metrics, we further demonstrate state-of-the-art person re-ID performance, which also validates the general applicability of the proposed method
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Xin, Bo
|e verfasserin
|4 aut
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| 700 |
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|a Wang, Jingdong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Dongdong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Bao, Jianmin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hua, Gang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Houqiang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g PP(2020) vom: 13. Okt.
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|x 1941-0042
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|g month:10
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|u http://dx.doi.org/10.1109/TIP.2020.3029415
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