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- W2963579011 abstract "The combination of global and partial features has been an effective method to improve the precision for Person Re-identification. However, illumination, camera angle and pedestrian pose, etc. still have adverse effects on the retrieval results. In particular, a lot of background and other redundant information is contained in the boundingbox. Meanwhile, the part-based solutions are imprecise on account of unbalanced partitioning. In order to minimize the impact of these factors on the retrieval results, we introduced the pixel, channel attention modules and middle layer supervision into the ReID system to aggregate person features. In this paper, we propose a novel architecture for Person Re-Identification, with the pixel and channel attention modules that are beneficial for feature extraction. Comprehensive experiments results on the mainstream datasets including Market-1501, DukeMTMC-ReId, CUHK03-labeled and CUHK03-detected show that our method achieves better results." @default.
- W2963579011 created "2019-07-30" @default.
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- W2963579011 date "2019-01-01" @default.
- W2963579011 modified "2023-09-27" @default.
- W2963579011 title "Pixel and Channel Attention Network for Person Re-identification" @default.
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- W2963579011 doi "https://doi.org/10.1007/978-981-13-9917-6_10" @default.
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