AMFOR: Adaptive Multi-Granularity Fusion and Occlusion Reconstruction for Person Re-Identification

Occluded person re-identification (ReID) poses substantial challenges in computer vision, primarily due to incomplete information and occlusion interference. Although Transformer architectures have become dominant in ReID due to their strong feature modeling capabilities, their lack of an adaptive weight allocation mechanism for multi-granularity feature processing limits their ability to extract generalizable and robust features. Recently, Masked Image Modeling (MIM) has demonstrated considerable promise in visual tasks, but its integration into ReID models remains underexplored. This paper presents AMFOR (Adaptive Multi-granularity feature Fusion and Occlusion Reconstruction), a novel framework combining MIM and Transformer architectures. AMFOR consists of three key components: AMFF-Encoder, HPR-Decoder, and Teacher-Student Decoder. The AMFF-Encoder enables adaptive fusion of multi-granularity features through learnable queries, allowing interaction between text-visual features and visual features extracted from multiple Transformer layers. The HPR-Decoder conceptualizes occluded regions in pedestrian images as reconstructable patches, guiding the encoder to extract more discriminative features through reconstruction. Additionally, the self-distillation teacher-student decoder is employed to refine pedestrian part features, further optimized by the proposed AMGDLoss. This paper represents the first successful implementation of the MIM mechanism in person ReID models. Empirical evaluations on five benchmark datasets, covering both occluded (Occluded-DukeMTMC, Occluded-REID, and P-DukeMTMC-reID) and complete (Market-1501 and DukeMTMC-reID) scenarios, demonstrate that AMFOR outperforms existing state-of-the-art methods in person ReID. Our code is available at https://github.com/Guangdeng-Li/AMFOR