通过对比和快速学习来增强零射击姿势检测
Zhenyin Yao1, Wenzhong Yang1, Fuyuan Wei1
1School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China.
Entropy (Basel, Switzerland)
|April 26, 2024
概括
这项研究引入了一种新的零射击姿势检测模型,该模型使用封闭的多层感知器 (gMLP) 动态链接提示到特定实例. 这种方法改善了在社交网络中对公众论分析的概括性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 社交网络分析 社交网络分析
背景情况:
- 公众论在社交网络上迅速演变,尤其是在意想不到的事件发生时.
- 零射击姿势检测对于分析没有特定训练数据的意见至关重要,需要具有强烈概括性的模型.
- 现有的基于提示的学习方法经常使用统一的提示,忽视特定实例的关系并限制与任务相关的知识获取.
研究的目的:
- 开发一个立场检测模型,以动态增强提示和特定实例之间的相关性,以实现零射击场景.
- 提高模型在识别与目标相关的立场特征方面的概括能力.
- 为了解决一些镜头的文本分类中统一的提示模式的局限性.
主要方法:
- 引入了使用封闭多层感知器 (gMLP) 和定制提示学习策略的姿势检测模型.
- 在gMLP中使用控制门机制来调节基于实例语义的提示符影响,加强实例-提示符连接.
- 集成的对比学习来增强特征表示的歧视力.
主要成果:
- 拟议的模型在零射击姿势检测场景中表现出有效性.
- 在VAST基准数据集上,与JointCL模型相比,实现了1.3%的改进.
- 在VAST和SEM16数据集上的实验评估验证了该方法的性能.
结论:
- 动态增强实例提示符相关性对于有效的零射击立场检测至关重要.
- 提出的基于gMLP的模型与对比学习提供了一个强大的解决方案,用于分析社会网络中的论.
- 该方法在缺乏具体的培训示例的情况下,有望改善姿势检测.
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