
Fewshot Classification of Documents with High Intraclass Variance
Document understanding using ResNet-50 and DiT with embedding-based few-shot learning (triplet loss), achieving strong similarity search performance, followed by insights on classification–few-shot overlap and multimodal fusion of ResNet visual features with BERT embeddings for improved generalization.






