PDRNet: Pinwheel‐Guided Dynamic Representation Learning for Visible‐Infrared Person Re‐Identification

ABSTRACT Visible‐infrared person re‐identification (VI‐ReID) is a cross‐modal retrieval task characterized by significant challenges, with the objective of precisely identifying and matching pedestrian instances across different spectral modalities, namely visible‐light and infrared imagery. The primary difficulties stem from substantial inter‐modal discrepancies and intra‐modal feature variations, which complicate effective cross‐modal matching. While existing approaches generally focus on embedding heterogeneous modal data into a unified feature space to extract shared representations, they often overlook the discriminative identity information embedded within modality‐specific features. To overcome this inherent limitation, we propose a novel pinwheel‐guided dynamic representation network (PDRNet), designed to mine and enhance the directional structural cues and scale‐sensitive discriminative features inherent in modality‐specific representations. Specifically, we integrate direction‐aware pinwheel convolution (PConv) into a two‐stream architecture to strengthen local structural representation and guide the learning of shared semantic features, thereby improving both the discriminability and structural modeling of modality‐specific information. Furthermore, to accommodate scale disparities across modalities and individuals, we incorporate a scale‐based dynamic loss (SD loss), which adaptively adjusts the loss weights related to scale and positional information. This mechanism mitigates the error amplification often observed in small‐scale samples and enhances both the discriminative power and robustness of cross‐modal matching across varying scales. We perform extensive experiments on multiple well‐established public benchmarks. The results consistently show that the proposed PDRNet achieves superior performance compared to existing methods in both recognition accuracy and cross‐modal matching effectiveness.