MambaReID: Exploiting Vision Mamba for Multi-Modal Object Re-Identification.

Abstract

Multi-modal object re-identification (ReID) is a challenging task that seeks to identify objects across different image modalities by leveraging their complementary information. Traditional CNN-based methods are constrained by limited receptive fields, whereas Transformer-based approaches are hindered by high computational demands and a lack of convolutional biases. To overcome these limitations, we propose a novel fusion framework named MambaReID, integrating the strengths of both architectures with the effective VMamba. Specifically, our MambaReID consists of three components: Three-Stage VMamba (TSV), Dense Mamba (DM), and Consistent VMamba Fusion (CVF). TSV efficiently captures global context information and local details with low computational complexity. DM enhances feature discriminability by fully integrating inter-modality information with shallow and deep features through dense connections. Additionally, with well-aligned multi-modal images, CVF provides more granular modal aggregation, thereby improving feature robustness. The MambaReID framework, with its innovative components, not only achieves superior performance in multi-modal object ReID tasks, but also does so with fewer parameters and lower computational costs. Our proposed MambaReID's effectiveness is validated by extensive experiments conducted on three multi-modal object ReID benchmarks.