对称的双向知识对齐用于基于零拍摄草图的图像检索
Decheng Liu1, Xu Luo1, Chunlei Peng1
1State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University, Xi'an, 710071, Shaanxi, PR China.
概括
本研究引入了对称双向知识对齐 (SBKA),通过对模型之间的知识进行对齐来改进基于零拍摄草图的图像检索 (ZS-SBIR). 这种方法增强了跨模式匹配,以便在未见的类别中更好地泛化.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于零拍摄草图的图像检索 (ZS-SBIR) 面临着挑战,原因是显著的交叉模式差异.
- 现有的方法往往缺乏教师和学生模型之间的有效的双向知识对齐,限制了概括.
研究的目的:
- 为 ZS-SBIR 提出一个新的对称双向知识对齐 (SBKA) 框架.
- 通过有效地调整教师和学生模型之间的知识来提高ZS-SBIR模型的通用性.
主要方法:
- 开发了一个对称的双向知识对齐学习框架 (SBKA).
- 实施一对多集群跨模式匹配方法,以利用类内关系和减轻模式差距.
主要成果:
- 拟议的SBKA算法在代表性的ZS-SBIR数据集 (Sketchy Ext,TU-Berlin Ext,QuickDraw Ext) 上取得了卓越的性能.
- 在减少 ZS-SBIR 任务中模式差距的不利影响方面表现出有效性.
结论:
- SBKA框架有效地学习相互歧视的信息,以改善知识对齐.
- 新的匹配策略提高了ZS-SBIR的性能,超过了最先进的方法.
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