Fast Residual Network for Person Re-identification
Authors: 
Peng Yu, Yan PiaoAuthors Info & Claims
Pages 92 - 97
Abstract
This paper proposes a method for fast residual network learning features to enhance the model effect, which can effectively improve the effect of person re-identification. Unlike most other person re-identification methods, the proposed model is more general and can be used in many applications as it does not need to be optimized for consistency with the characteristics of a specific dataset. In addition, the model is modular and easy to be included in any other network for improving network performance. The results tested on Market1501, DukeMTMC-reID, CUHK03 datasets show that the model is better to obtain effective features.
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Published In
September 2022
215 pages
ISBN:9781450397551
DOI:10.1145/3568199
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Published: 06 March 2023
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MLMI 2022
MLMI 2022: 2022 5th International Conference on Machine Learning and Machine Intelligence
September 23 - 25, 2022
Hangzhou, China
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