对实例分割模型的稳定性进行基准测试
IEEE transactions on neural networks and learning systems
|September 18, 2023
概括
本研究评估实例细分模型的现实世界使用. 组规范化 (GN) 提高了对图像损坏的稳定性,而批量规范化 (BN) 则有助于跨数据集的泛化.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 图像分析 图像分析
背景情况:
- 实例细分模型对于现实应用至关重要.
- 在现实世界的图像损坏和域外数据下评估模型性能对于部署至关重要.
- 域名适应是改善泛化能力的关键领域.
研究的目的:
- 综合评估实例细分模型与现实世界的图像损坏和域外数据集.
- 评估各种建筑选择和培训策略对模型稳定性和概括性的影响.
- 为选择或设计实用的强大的实例细分模型提供见解.
主要方法:
- 基准测试最先进的实例细分架构,骨干和规范化层.
- 从头开始训练的模型与预训练的网络进行比较.
- 研究多任务训练对强度和通用性的影响.
- 评估损坏和域外图像集合的性能.
主要成果:
- 组正常化 (GN) 增强了对图像腐败的稳定性.
- 批量规范化 (BN) 提高了不同数据集的概括性,具有不同的特征统计数据.
- 单阶段探测器对更大的图像分辨率的概括性很差,与多阶段探测器不同.
- 预训练的模型和多任务训练影响了强度和通用性.
结论:
- 模型设计选择显著影响实例细分中的稳定性和概括性.
- 对于不同类型的性能退化,GN和BN提供了明显的优势.
- 多级探测器更适应不同的图像分辨率.
- 这一基准指导开发和选择可靠的实例细分模型,用于现实世界的部署.
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