ReMamba: a hybrid CNN-Mamba aggregation network for visible-infrared person re-identification.

ABSTRACT: Visible-Infrared Person Re-identification (VI-ReID) has been consistently challenged by the significant intra-class variations and cross-modality differences between different cameras. Therefore, the key lies in how to extract discriminative modality-shared features. Existing VI-ReID methods based on Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have shortcomings in capturing global features and controlling computational complexity, respectively. To tackle these challenges, we propose a hybrid network framework called ReMamba. Specifically, we first use a CNN as the backbone network to extract multi-level features. Then, we introduce the Visual State Space (VSS) model, which is responsible for integrating the local features output by the CNN from lower to higher levels.

SUBMITTER: Geng H 

PROVIDER: S-EPMC11599763 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature