The Devil is in the Few Shots: Iterative Visual Knowledge Completion for Few-shot Learning

arXiv:2404.09778v1 Announce Type: new
Abstract: Contrastive Language-Image Pre-training (CLIP) has shown powerful zero-shot learning performance. Few-shot learning aims to further enhance the transfer capability of CLIP by giving few images in each class, aka 'few shots'. Most existing methods either implicitly learn from the few shots by incorporating learnable prompts or adapters, or explicitly embed them in a cache model for inference. However, the narrow distribution of few shots often contains incomplete class information, leading to biased visual knowledge with …

arxiv cs.cv few-shot few-shot learning iterative knowledge type visual