CARE: Constrained Attention Refinement for Fine-Grained Visual Classification via Teacher-Student Distillation

CARE introduces a constrained attention refinement framework that uses teacher‑student distillation to improve fine‑grained visual classification while maintaining interpretability. The method keeps the final prediction and explanation within a class‑specific attention student, while a training‑only auxiliary teacher reads selected intermediate DINOv2 layers with learnable queries to fuse multi‑level representations and transfer class‑discriminative knowledge. Additional diversity and sparsity regularizers refine the student’s attention heads, reducing redundancy and encouraging compact trait localization, achieving 78.5% Top‑1 accuracy on CUB and demonstrating faithful explanations via insertion and deletion metrics.

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