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 high risk
of misclassification. To tackle this problem, recent methods propose to
supplement visual knowledge by generative models or extra databases, which can
be costly and time-consuming. In this paper, we propose an Iterative Visual
Knowledge CompLetion (KCL) method to complement visual knowledge by properly
taking advantages of unlabeled samples without access to any auxiliary or
synthetic data. Specifically, KCL first measures the similarities between
unlabeled samples and each category. Then, the samples with top confidence to
each category is selected and collected by a designed confidence criterion.
Finally, the collected samples are treated as labeled ones and added to few
shots to jointly re-estimate the remaining unlabeled ones. The above procedures
will be repeated for a certain number of iterations with more and more samples
being collected until convergence, ensuring a progressive and robust knowledge
completion process. Extensive experiments on 11 benchmark datasets demonstrate
the effectiveness and efficiency of KCL as a plug-and-play module under both
few-shot and zero-shot learning settings. Code is available at
this https URL

本文提出了一种迭代式视觉知识完善（KCL）方法，通过正确利用未标记样本来补充视觉知识，以应对少样本学习中存在的分类偏差问题。经过多次迭代，收集更多样本以逐步稳定和完善视觉知识。实验结果表明，KCL 在少样本和零样本学习设置下作为即插即用模块具有有效性和高效性。