CLIP-Guided Label-Free Discriminative Region Scoring for Fine-Grained Classification
A unified CLIP-guided label-free region scoring framework for fine-grained classification is proposed, and random-crop-based pseudo-label scoring consistently outperforms SAM-based scoring across all datasets, suggesting that random crops preserve surrounding information and provide more stable semantic context when ps...