HomLLM:利用语义同源关系通过大型语言模型进行细粒度鸟类图像分类
IEEE transactions on neural networks and learning systems
|October 28, 2025
概括
这项研究介绍了HomLLM,一种使用大型语言模型 (LLM) 进行准确细粒度鸟类图像分类的新方法. HomLLM有效地识别了鸟类的关键特征和分类学标记,改善了在具有挑战性的环境中的识别.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 细粒度鸟类图像分类 (FBIC) 面临着各种姿势,相似物种和遮等挑战.
- 准确识别危鸟类对保护工作至关重要.
研究的目的:
- 开发一种新的方法,HomLLM,使用大型语言模型 (LLM) 进行有效的细粒度鸟类分类.
- 通过学习语义同义关系来解决FBIC的局限性.
主要方法:
- 拟议的HomLLM模型利用语义同义关系表示学习.
- 介绍了语义同源生成 (SHG) 和同源关系挖掘 (HRM) 模块.
- 利用LLM来实现多细分的特征描述和层次的交叉模式交互.
主要成果:
- 在不同姿势的关键鸟类区域中确定了不变的同质性.
- 建立了同类关系作为类似鸟类类别的基本分类标记.
- 与最先进的方法相比,HomLLM在CUB-200-2011和NABirds数据集上表现出卓越的性能.
结论:
- 拟议的HomLLM通过学习不变结构对应函数,有效地解决了FBIC的挑战.
- 来自LLM的语义同义表示增强了鸟类物种的可区分性.
- 这种方法为自动化物种识别和保护提供了一个有希望的方向.
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