EPIR: An Efficient Patch Tokenization, Integration and Representation Framework for Micro-expression Recognition
EPIR: 一种高效的补丁分词、整合与表示框架用于微表情识别
机构 * School of Software, Northwestern Polytechnical University(西北工业大学软件学院) ; School of Computer Science, Northwestern Polytechnical University(西北工业大学计算机学院) ; School of Computer Science and Technology, Xi'an Jiaotong University(西安交通大学计算机科学与技术学院) ; School of Science, Edith Cowan University(埃迪斯科文大学理学院)
AI总结 本文提出EPIR框架,通过双范数位移分词模块、token整合模块和判别token提取器,提升微表情识别性能并降低计算复杂度,实验表明在多个数据集上均优于现有方法。