Scale-Invariant Siamese Network For Person Re-Identification

Zhang, Yunzhou; Shi, Weidong; Liu, Shuangwei; Bao, Jining; Wei, Ying

doi:10.1109/icip40778.2020.9191079

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…In recent years, deep learning methods are usually used to extract discriminative features [4] for person re-id task. For * Corresponding author instance, the image size [5], scale [6], alignment [7], segmentation [8] are taken into account to improve the feature representation. Spatial transformer network (STN) [9] and the partial framework [10] are designed to observe competition between STN module and ReID module.…”

Section: Introductionmentioning

confidence: 99%

PCNET: Parallelly Conquer the Large Variance of Person Re-Identification

Wang

You

Leng

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Person re-identification has a wide range of applications, and many state-of-the-art methods are proposed to solve the problem under specific scenarios. However, it is still a challenging issue because of the large variance in practical applications, such as pose variations, misalignment, and image noises. In this paper, Parallelly Conquer Net (PCNet) is proposed to deal with large variance in a parallel manner. PCNet consists of three module: Pose Adaptation Module (PAM), Global Alignment Module (GAM), and Pixel-Wised Attention Module (PWAM). Each module is designed to deal with a sub-variance independently. Furthermore, the generated features are aggregated by parallel branches to utilize complementary information among them. Extensive experiments on three benchmarks (Market-1501, DukeMTMC-reID, and CUHK03) demonstrate the effectiveness of the method. The results show that PCNet can significantly improve the performance of person re-identification.

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