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CTFS:一个整合的变压器框架,例如和语义细分任务
Kun Dai1, Fuyuan Qiu2, Hongbo Gao2
1State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150006, China; Yangtze River Delta HIT Robot Technology Research Institute, Wuhu, 241000, China.
概括
本研究介绍了用于同时实例和语义细分的整合变压器框架 (CTFS). CTFS采用新的策略来优化共享参数和解决梯度冲突,提高计算机视觉模型的性能.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 机器学习 机器学习
背景情况:
- 实例和语义细分是计算机视觉的关键任务.
- 现有的统一变压器框架与并发的多任务优化和梯度冲突作斗争.
研究的目的:
- 开发一个合并的变压器框架 (CTFS) 以实现高效的同时实例和语义细分.
- 解决多任务学习中优化共享参数和减轻梯度冲突的挑战.
主要方法:
- 引入了以亲和为导向的共享策略 (AGSS),用于分阶段学习任务共享参数比例和分布.
- 提出了一种细粒度梯度校正策略 (FGRS),以解决反向传播期间的元素智能梯度冲突.
- 在标准的Swin变压器架构上构建了CTFS框架.
主要成果:
- 在实例细分 (COCO数据集) 和语义细分 (ADE20K数据集) 上,CTFS取得了令人印象深刻的表现.
- 通过使用共享参数比例作为预先知识,AGSS战略减少了网络优化的难度.
- 在元素层面上,FGRS战略有效地缓解了梯度冲突.
结论:
- 合并的变压器框架 (CTFS) 为同时实例和语义细分提供了有效的解决方案.
- 拟议的AGSS和FGRS战略显著改善了网络优化和梯度冲突解决.
- 在不复杂化底层Swin变压器架构的情况下,CTFS表现出强大的性能.
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