Abstract: In deep-learning person re-identification algorithms, channel characteristics may be neglected, leading to a degraded model-expression ability. Address to the problem, we choose the ResNeSt50 as backbone network, and add an SE block to the end of residual blocks by using characteristics of SENet channel attention for enhancing features extraction of channels in networks. In addition, due to lack of control factors, ReLU function may reduce the correct responses of different feature graphs to activation values. Thus, we present two new activation functions. One is named as Weighted ReLU (WReLU) by combining ReLU with weight bias term, which can effectively improve feature selection ability in neural networks, and the other is Leaky Weighted ReLU (LWReLU), which is applied in Split-Attention and SE block, and enables Split-Attention to promote the weight learning ability from feature maps. Moreover, a new loss function with circle loss is also proposed for optimizing the convergence of objective function. Experimental results show that the proposed algorithm outperforms original backbone by 19.08%, 0.98%, and 2.02% in Rank-1, and 17.13%, 2.11%, and 2.56% in mAP respectively on CUHK03-NP, Market1501, and DukeMTMC-ReID datasets.

Key words: group convolution, channel attention, rectified linear unit, activation function, dynamic learning factor