Object re-identification using Kolmogorov-Arnold attention networks

Kolmogorov-Arnold Networks (KAN) have gained significant attention due to their exceptional accuracy and adaptability. In the field of object re-identification, attention mechanisms are crucial for enhancing model performance. Building on this foundation, we introduce an innovative approach that employs KAN to design these mechanisms. Central to our advancement is the KAN spatial-channel merge attention (KAN-SCMA) module, which integrates seamlessly into existing residual networks to form KAN attention networks (KAAN). This module consists of two sub-modules: the KAN spatial-channel refine (KSCR) and the KAN spatial-channel fusion (KSCF). The KSCR module refines input feature maps by leveraging the ability of KAN to manage high-dimensional data with the combination of spline basis functions, preserving essential information during compression. Meanwhile, the KSCF module utilizes the flexible nonlinear mapping of KAN through learnable activation functions, capturing complex patterns and nuances in input data during spatial and channel attention. Our comprehensive theoretical analysis of the attention design of KAN, along with extensive experiments on the Market-1501, MSMT17, and VeRi-776 datasets, demonstrates that KAAN outperforms current state-of-the-art methods in object re-identification.