语义意识的消息广播,以实现高效的无监督域名适应
概括
视觉变压器在与分销转移作斗争. 语义意识的消息广播 (SAMB) 通过实现灵活的特征对齐来提高无监督域的适应性,增强分布外数据的概括性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 视觉变压器在计算机视觉任务中表现有前途,但由于分布转移,在分布之外的数据上表现不佳.
- 目前使用单个全球类令牌进行特征对齐的无监督域适应 (UDA) 方法缺乏灵活性,无法捕捉区域语义.
研究的目的:
- 提出语义意识的信息广播 (SAMB) 以更具信息性和灵活的功能对齐在视觉转换器的UDA.
- 通过启用语义意识的自适应消息广播来增强对齐功能的丰富性.
主要方法:
- 引入学习小组令牌,以汇总全球信息,并为不同语义区域的适应性消息广播提供便利.
- 研究了基于对抗的特征对齐 (ADA) 和基于伪标签的自我训练 (PST) 对于UDA.
- 实施了结合ADA和PST的两阶段培训策略.
主要成果:
- SAMB使组代币能够学习信息丰富和多样化的信息,以便有效地对准域名.
- 拟议的两阶段培训策略显著提高了视觉转换器的适应能力.
- 在DomainNet,OfficeHome和VisDA-2017数据集上的实验验证了拟议方法的有效性.
结论:
- 语义意识的信息广播 (SAMB) 有效地解决了UDA中的视觉转换器的概括限制.
- 在两阶段的培训方法中,SAMB与ADA和PST的结合为无监督域调整提供了强大的解决方案.
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