Weakly supervised learning has recently achieved considerable success in
reducing annotation costs and label noise. Unfortunately, existing weakly
supervised learning methods are short of ability in generating reliable labels
via pre-trained vision-language models (VLMs). In this paper, we propose a
novel weakly supervised labeling setting, namely True-False Labels (TFLs) which
can achieve high accuracy when generated by VLMs. The TFL indicates whether an
instance belongs to the label, which is randomly and uniformly sampled from the
candidate label set. Specifically, we theoretically derive a risk-consistent
estimator to explore and utilize the conditional probability distribution
information of TFLs. Besides, we propose a convolutional-based Multi-modal
Prompt Retrieving (MRP) method to bridge the gap between the knowledge of VLMs
and target learning tasks. Experimental results demonstrate the effectiveness
of the proposed TFL setting and MRP learning method. The code to reproduce the
experiments is at this https URL

我们提出了一个新的弱监督标签设置，即真假标签（TFLs），通过预训练的视觉语言模型（VLMs）生成的 TFLs 可以实现高准确性。我们理论上推导了一种风险一致估计器，以探索和利用 TFLs 的条件概率分布信息。此外，我们提出了一种基于卷积的多模态提示检索（MRP）方法，以弥合 VLMs 的知识与目标学习任务之间的差距。实验结果证明了所提出的 TFL 设置和 MRP 学习方法的有效性。