利用视觉语言模型作为弱标注器在主动学习中的应用
Leveraging Vision-Language Models as Weak Annotators in Active Learning
AI总结：
本文提出结合细粒度人工标注与粗粒度VLM生成弱标注的主动学习框架，通过实例级标注分配和系统噪声建模，在CUB200和FGVC-Aircraft数据集上优于现有方法。
摘要：
主动学习旨在通过有限标注预算选择有信息量的样本进行监督以降低标注成本。本文研究了如何利用视觉语言模型（VLMs）进一步减少对昂贵人工标注的依赖。我们发现，在细粒度识别任务中，VLMs的可靠性随标签粒度显著变化：它们在细粒度标签上表现不佳，但在粗粒度标签上能提供准确的标签。基于这一特性，我们提出一个主动学习框架，通过实例级标签分配结合细粒度人工标注与粗粒度VLM生成的弱标签。我们进一步利用一小部分可信的完整标签建模VLM生成标签的系统噪声。在CUB200和FGVC-Aircraft上的实验表明，所提出的框架在相同标注预算下始终优于现有主动学习方法。
英文摘要：
Active learning aims to reduce annotation cost by selectively querying informative samples for supervision under a limited labeling budget. In this work, we investigate how vision-language models (VLMs) can be leveraged to further reduce the reliance on costly human annotation within the active learning paradigm. To this end, we find that the reliability of VLMs varies significantly with label granularity in fine-grained recognition tasks: they perform poorly on fine-grained labels but can provide accurate coarse-grained labels. Leveraging this property, we propose an active learning framework that combines fine-grained human annotations with coarse-grained VLM-generated weak labels through instance-wise label assignment. We further model the systematic noise in VLM-generated labels using a small set of trusted full labels. Experiments on CUB200 and FGVC-Aircraft show that the proposed framework consistently outperforms existing active learning methods under the same annotation budget.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
