Multi-Scale Cross-Modal Collaborative Reconstruction Network

Few-shot fine-grained image classification faces significant challenges due to subtle inter-class distinctions and limited annotated samples, where conventional methods often struggle to comprehensively exploit multi-granularity semantic cues under single-scale feature fusion or unimodal representation constraints. To address this, this paper proposes a Multi-Scale Cross-Modal Collaborative Reconstruction Network (MSCMCRN), which synergistically integrates hierarchical feature aggregation, cross-modal interaction, and contrastive-guided optimization. Our framework first introduces a pyramid feature adaptation module that dynamically fuses multi-scale representations through channel-wise and spatial self-attention mechanisms, enabling joint modeling of local discriminative patterns and global structural coherence. A bidirectional crossmodal attention mechanism is then designed to explicitly capture interdependencies between channel-specific attributes and spatialaware contours, effectively enhancing feature discriminability through mutual reinforcement. Furthermore, this paper proposes a collaborative optimization paradigm that unifies bidirectional feature reconstruction consistency with contrastive metric learning, simultaneously ensuring intra-class compactness and inter-class separability in the embedding space. Extensive evaluations on three challenging fine-grained benchmarks (CUB-200-2011, Stanford Cars, NABirds) demonstrate that MSCMCRN consistently surpasses state-of-the-art approaches in classification tasks. The results underscore the effectiveness of hierarchical multi-modal fusion in mitigating information underutilization and the critical role of contrastive constraints in alleviating few-shot overfitting, providing new insights for openenvironment fine-grained recognition scenarios.