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Person Re-identification by Local Maximal Occurrence Representation and Metric Learning(LOMO+XQDA)
時間 2021-01-06
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2015年,學術界主流都在DL,而這篇文章卻用傳統方法達到了遠超state of the art的結果: Retinex transform預處理: 爲了解決不同攝像頭下光照條件變化很大:作者在特徵提取前進行了預處理,使用的是multiscale Retinex algorithm(這個算法之前做圖像增強的時候接觸過) ,Retinex algorithm能夠增強光照陰影區域的色彩信息,有利於重識
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相關文章
1.
Re-ID: Person Re-identification by Local Maximal Occurrence Representation and Metric Learning 論文解析
2.
DIVERSITY REGULARIZED METRIC LEARNING FOR PERSON RE-IDENTIFICATION(ICIP2016)
3.
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4.
1505.Deep Ranking for Person Re-identification via Joint Representation Learning論文筆記
5.
1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
6.
Improving Person Re-identification by Attribute and Identity Learning
7.
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Re-ID: Learning Deep Feature Representation with Domain Guided Dropout for Person Re-identifiation
9.
2019 IET之ReID:HPILN: a feature learning framework for cross-modality person re-identification
10.
Self-Supervised Representation Learning
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