SketchTrans:解原型学习与变压器的素描-照片识别
IEEE transactions on pattern analysis and machine intelligence
|November 27, 2023
概括
本研究介绍了SketchTrans+,这是一个用于素描-照片识别的新型变压器模型. 它通过对齐跨模式表示来有效地解决信息不对称性,提高匹配精度.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 素描-照片识别由于模式差异而面临信息不对称.
- 像CNN和GAN这样的现有方法在歧视性和生成噪音方面存在局限性.
研究的目的:
- 开发一个与信息结合的素描变压器 (SketchTrans+),用于改进素描-照片识别.
- 为应对草图和照片模式之间信息不对称的挑战.
主要方法:
- 设计了一个带有动态辅助草图 (A草图) 的非对称解方案,以对齐模式表示.
- 采用一种模式意识的原型对比学习方法来挖掘共享信息.
- 使用变压器架构进行歧视性视觉建模.
主要成果:
- 拟议的SketchTrans+方法在类别和实例级草图数据集上表现出卓越的性能.
- 不对称的解实际上转移了与草图无关的知识,以弥补缺少的草图信息.
- 模式意识的原型增强了代表性模式共享信息的挖掘.
结论:
- 通过解决信息不对称性,SketchTrans+为素描-照片识别提供了一种新有效的方法.
- 跨模式解的原型学习框架显著提高了匹配准确性和可辨别性.
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