快速:对于域自适应对象检测的前景意识主动自我训练
Dan Zhang1, Hongmin Deng1, Hailin Wang1
1Southwestern University of Finance and Economics, China.
概括
具有前景意识的主动自我训练 (FAST) 通过选择性标记信息目标数据来改善域自适应对象检测. 这种方法在新数据集上增强了对象探测器,并且注释成本最小,性能优于现有的方法.
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:
- 域自适应对象检测 (DAOD) 在未标记的目标数据上使用标记的源数据训练模型.
- 平均教师的自我训练对DAOD有效,但受到杂的伪标签的阻碍.
- 主动域调整 (ADA) 选择性地注释信息数据以降低成本,但其在DAOD中的使用是有限的.
研究的目的:
- 开发一个有效的主动学习框架,用于域自适应对象检测.
- 解决DAOD的教师自我培训中杂的伪标签的挑战.
- 通过选择最有价值的目标样本来最大限度地降低DAOD中的注释成本.
主要方法:
- 建议为积极的DAOD提供前沿意识的积极自我训练 (FAST) 框架.
- 引入了前景多样性集群采样 (FDCS) 以最大限度地提高前景对象多样性.
- 实施教师-学生差异不确定性抽样 (TDUN) 来识别不确定的预测.
- 利用了与专用抽样模型脱的积极学习范式.
主要成果:
- FAST显著提高了目标域上的对象检测性能.
- 拟议的抽样策略有效地识别了有信息性的目标样本.
- 实验结果表明,在多个DAOD数据集中,性能优越.
- 该方法在具有挑战性的场景中成功地弥合了领域差距.
结论:
- FAST建立了一个有效的框架,用于主动域自适应对象检测.
- 结合FDCS和TDUN采样策略,优化了注释工作.
- 拟议的方法提供了一个有前途的解决方案,以最小的注释成本来改进DAOD.
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