DMFNet: exploring diverse mid-feature for visible-infrared person re-identification
TL;DR
DMFNet (Diverse Mid-feature Network) is presented, a novel deep learning architecture that effectively harnesses intermediate shared features to bridge this cross-modal gap and enhances cross-modal matching capabilities but also provides interpretable feature visualizations, offering valuable insights into the network's decision-making process.
Abstract
Visible-infrared person re-identification remains a challenging task due to inherent modality discrepancies between RGB and infrared images. Existing methods often struggle to effectively capture both modality-specific and modality-invariant features simultaneously, limiting their cross-modal matching performance. This paper presents DMFNet (Diverse Mid-feature Network), a novel deep learning architecture that effectively harnesses intermediate shared features to bridge this cross-modal gap. DMFNet integrates two key modules: a Multi-layer Feature Cascade Module (MFCM) that aggregates discriminative features across different network stages, and a Dual Feature Generation Module (DFGM) that produces diverse intermediate representations through Instance-Batch Normalization variants. Extensive experiments on the SYSU-MM01 and RegDB datasets demonstrate that DMFNet achieves state-of-the-art performance, with significant improvements in Rank-1 accuracy (up to 8.2% on SYSU-MM01 and 6.5% on RegDB) and mean Average Precision (mAP) over existing methods. Our approach not only enhances cross-modal matching capabilities but also provides interpretable feature visualizations, offering valuable insights into the network's decision-making process. These results pave the way for more robust person re-identification systems in real-world surveillance scenarios, particularly in low-light conditions where traditional visible-only systems often fail.