Channel convolution residual block for person re-identification

Zhengxin Zeng; Zhuqing Jiang; Aidong Men; Guodong Ju

doi:10.1117/12.2557238

3 January 2020 Channel convolution residual block for person re-identification

Zhengxin Zeng, Zhuqing Jiang, Aidong Men, Guodong Ju

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113730B (2020) https://doi.org/10.1117/12.2557238
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

In previous works, the channel attention mechanism has been widely used in person re-identification. However, the channel attention mechanism completely compresses the spatial dimension during calculation, which harms the diversity of the channel information over different pixels. In this paper, a channel convolution residual block is proposed for more detailed inter-channel correlation modeling. First, we preserve spatial context information when introducing the channel dependency, which enables pixel-wise inter-channel correlation modeling. At the same time, a bottleneck strategy is used to reduce parameters in the spatial dimension. Second, the channel convolution instead of the fully connected layer is employed to reduce the parameters in the channel dimension. In addition, the inter-channel correlation is merged into the backbone network directly in the form of residual, and thus the block can be embedded in any deep neural networks. Experiments on Market1501 and DukeMTMC-ReID datasets demonstrate that the channel convolution residual block improves the accuracy of person re-identification task effectively.

Citation Download Citation

Zhengxin Zeng, Zhuqing Jiang, Aidong Men, and Guodong Ju "Channel convolution residual block for person re-identification", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730B (3 January 2020); https://doi.org/10.1117/12.2557238

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KEYWORDS

Convolution

Cameras

Computer vision technology

Data modeling

Feature extraction

Machine vision

Neural networks

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