Active Learning for Vision-Language Models

• URL: http://arxiv.org/abs/2410.22187v1
• Date: Tue, 29 Oct 2024 16:25:50 GMT
• Title: Active Learning for Vision-Language Models
• Authors: Bardia Safaei, Vishal M. Patel,
• Abstract summary: We propose a novel active learning (AL) framework that enhances the zero-shot classification performance of vision-language models (VLMs) Our approach first calibrates the predicted entropy of VLMs and then utilizes a combination of self-uncertainty and neighbor-aware uncertainty to calculate a reliable uncertainty measure for active sample selection. Our experiments show that the proposed approach outperforms existing AL approaches on several image classification datasets.
• Score: 29.309503214127016
• License:
• Abstract: Pre-trained vision-language models (VLMs) like CLIP have demonstrated impressive zero-shot performance on a wide range of downstream computer vision tasks. However, there still exists a considerable performance gap between these models and a supervised deep model trained on a downstream dataset. To bridge this gap, we propose a novel active learning (AL) framework that enhances the zero-shot classification performance of VLMs by selecting only a few informative samples from the unlabeled data for annotation during training. To achieve this, our approach first calibrates the predicted entropy of VLMs and then utilizes a combination of self-uncertainty and neighbor-aware uncertainty to calculate a reliable uncertainty measure for active sample selection. Our extensive experiments show that the proposed approach outperforms existing AL approaches on several image classification datasets, and significantly enhances the zero-shot performance of VLMs.

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