Search papers, labs, and topics across Lattice.
This paper introduces the Mixture of Enhanced-View Experts (EV-MoE) approach to improve multi-query vehicle re-identification (ReID) by enhancing feature representation from diverse views and integrating these features through a mixture of experts framework. The method addresses the limitations of existing techniques that fail to adequately fuse features and capture cross-view relationships, leading to significant improvements in robustness. The authors also present the LCRI-1K dataset, a large-scale benchmark for evaluating multi-query ReID performance across a complex real-world setting with over 107,000 images and 1,090 identities.
EV-MoE not only enhances feature representation but also introduces a large-scale benchmark that redefines multi-query vehicle ReID evaluation in complex environments.
Multi-query vehicle ReID aims to leverage complementary information from diverse views for robust feature learning. However, current methods suffer from simplistic feature fusion and thus easily ignores some important view information and cross-view relationships. To handle these problems, this work presents a novel approach called Mixture of Enhanced-View Experts (EV-MoE), which enhances the feature representation of each view and efficiently integrate the view-specific enhanced features by MoE, for robust multi-query ReID. In particular, we design a mixture of enhanced-view experts module, which consists of two parts including view-specific feature enhancement sub-Module (VFEM) and dynamic multi-view fusion sub-Module (DMFM). Moreover, we further introduce Multi-view Alignment Loss (MAL), which aligns features through bidirectional crossview contrastive learning and reconstruction constraints, addressing the challenges of consistency between multi-query features and single-image features. In addition, to evaluate multi-query ReID in real-world environments, we collect LCRI-1K, a largescale vehicle ReID dataset with 1,090 identities, 107,805 images, across 23,637 cameras, where each vehicle appears in an average of 67.5 cameras, providing a comprehensive benchmark to test the robustness in complex environments. Extensive experiments demonstrate the robustness of CAFNet in addressing the multiquery vehicle ReID problem. The code is available at https: //github.com/xiaozhen28/CAFNet.
