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

• URL: http://arxiv.org/abs/2309.14928v3
• Date: Tue, 30 Jul 2024 08:39:52 GMT
• Title: Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models
• Authors: Eman Ali, Muhammad Haris Khan,
• Abstract summary: 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. NtUA achieves superior performance consistently across multiple widely adopted benchmarks.
• Score: 8.59772105902647
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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