用视觉语言模型对零射击和少数射击的多式联运塑料废物进行分类
Iman Ranjbar1, Yiannis Ventikos2, Mehrdad Arashpour1
1Department of Civil Engineering, Monash University, Melbourne, VIC 3800, Australia.
Waste management (New York, N.Y.)
|April 24, 2025
概括
视觉语言模型 (VLMs) 为回收建筑塑料废物提供了一个可扩展的解决方案. 这些模型在用最小的数据对塑料类型进行分类时达到很高的准确性,提高了回收效率.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 建筑行业产生大量的塑料废物,需要有效的回收过程.
- 塑料废物的准确分类对于在回收过程中保持价值至关重要.
- 当前的监督深度学习模型需要大量的标记数据,限制了可扩展性和通用性.
研究的目的:
- 探索视觉语言模型 (VLMs) 的应用,以按树脂类型分类建筑和拆除塑料废物.
- 评估VLM在塑料废物分类零射击和少数射击学习场景中的有效性.
- 将VLM性能与完全监督的方法进行准确性,可扩展性和数据效率的比较.
主要方法:
- 使用先进的VLM来对塑料废物进行零射击分类,使用自然语言描述.
- 综合图像和文本模式在多式联络的少数镜头学习框架内.
- 进行了全面的实验,将VLM性能与数据密集型,完全监督的基线进行比较.
主要成果:
- VLMs证明了使用终止塑料的有效分类,培训数据很少或不存在.
- 在使用VLMs的零射击分类中实现了70.15%的准确性.
- 精度提高到85.07%,采用多式联络的少数拍摄学习,突出显示了数据效率和可扩展性的提升.
结论:
- VLMs为建筑和拆除塑料废物的分类提供了一种有希望的,数据效率高的方法.
- VLM的零射击和少数射击学习能力显著提高了塑料废物管理的可扩展性.
- 虚拟市场管理 (VLM) 是传统监管方法的可行替代方案,解决了数据采集和模型通用性的挑战.
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