In the midst of the rapid integration of artificial intelligence (AI) into real world applications, one pressing challenge we confront is the phenomenon of model drift, wherein the performance of AI models gradually degrades over time, compromising their effectiveness in real-world, dynamic environments. Once identified, we need techniques for handling this drift to preserve the model performance and prevent further degradation. This study investigates two prominent quality aware strategies to combat model drift: data quality assessment and data conditioning based on prior model knowledge. The former leverages image quality assessment metrics to meticulously select high-quality training data, improving the model robustness, while the latter makes use of learned feature vectors from existing models to guide the selection of future data, aligning it with the model's prior knowledge. Through comprehensive experimentation, this research aims to shed light on the efficacy of these approaches in enhancing the performance and reliability of semantic segmentation models, thereby contributing to the advancement of computer vision capabilities in real-world scenarios.

翻译：在人工智能快速融入实际应用的背景下，我们面临的一大紧迫挑战是模型漂移现象——即AI模型的性能随时间逐渐退化，削弱其在动态真实环境中的有效性。一旦检测到漂移，就需要采用相应技术加以处理，以维持模型性能并防止进一步衰退。本研究探索了两种应对模型漂移的典型质量感知策略：数据质量评估与基于先验模型知识的数据调节。前者利用图像质量评估指标精细筛选高质量训练数据，增强模型鲁棒性；后者则借助现有模型学习到的特征向量指导未来数据的选择，使其与模型的先验知识对齐。通过全面实验，本研究旨在揭示这些方法在提升语义分割模型性能与可靠性方面的有效性，从而为真实场景下的计算机视觉能力发展作出贡献。