基于变压器的方法通过对比学习进行零射击检测
Wei Liu1, Hui Chen1, Yongqiang Ma1
1Institute of Artificial Intelligence and Robotics, Xian Jiaotong University, Xian, Shaanxi 710049, P. R. China.
International journal of neural systems
|June 14, 2023
概括
通过使用变压器来更好地理解对象类之间的关系,Trans-ZSD框架改进了零射击检测 (ZSD). 这种单阶段模型提高了对未见物体的准确性,而不需要额外的训练数据.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 零射击检测 (ZSD) 旨在识别在训练期间未见过的类中的对象,依靠语义信息.
- 现有的双阶段 ZSD 方法与贫困地区提案,对阶级间相关性的不充分考虑,以及对被视为阶级的域偏差作斗争.
研究的目的:
- 引入Trans-ZSD,一种基于变压器的新型框架,用于改进零射击检测.
- 解决现有的ZSD方法的局限性,包括贫困地区提案和域偏差.
主要方法:
- 开发了一个单阶段的,基于变压器的多尺度上下文检测框架 (Trans-ZSD).
- 整合了前景和背景的分离,对比式学习以实现类间的独特性,以及共同的学习.
- 利用平衡损失来减轻泛化零射击检测 (GZSD) 中的域偏差.
主要成果:
- 在PASCAL VOC和MS COCO数据集上,Trans-ZSD在现有ZSD模型上显示出显著的性能改进.
- 该框架有效地利用了类间的相关性,并优化了歧视性学习的特征分布.
- 单阶段方法使得长期依赖性和上下文特征的编码具有较少的诱导偏差.
结论:
- 通过解决先前方法的关键局限性,Trans-ZSD为零射击检测提供了更强大,更准确的方法.
- 该框架显示出强大的通用化能力,特别是在具有挑战性的通用零射击检测 (GZSD) 设置中.
- 利用类间关系和减轻域偏差对于提高ZSD性能至关重要.
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