Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models

TL;DR

This paper introduces NtUA, a noise-tolerant unsupervised adapter for vision-language models that effectively learns from few unlabelled samples, improving zero-shot image classification without requiring labels.

Contribution

The paper proposes NtUA, a novel unsupervised adapter with adaptive cache formation and knowledge-guided refinement, enhancing scalability and robustness in visual recognition tasks.

Findings

Outperforms existing methods across multiple benchmarks.

Effectively handles pseudo-label noise with confidence weighting.

Leverages knowledge distillation for cache refinement.

Abstract

Recent advances in large-scale vision-language models have achieved impressive performance in various zero-shot image classification tasks. While prior studies have demonstrated significant improvements by introducing few-shot labelled target samples, they still require labelling of target samples, which greatly degrades their scalability and generalizability while handling various visual recognition tasks. We design NtUA, a Noise-tolerant Unsupervised Adapter that allows the learning of effective target models with few unlabelled target samples. NtUA works as a key-value cache that formulates visual features and predicted pseudo-labels of the few unlabelled target samples as key-value pairs. It consists of two complementary designs. The first is adaptive cache formation that combats pseudo-label noises by weighting the key-value pairs according to their prediction confidence. The second is knowledge-guided cache refinement, which refines pair values (i.e., pseudo-labels) and cache weights by leveraging knowledge distillation from large-scale vision language models. Extensive experiments show that NtUA achieves superior performance consistently across multiple widely adopted benchmarks.