  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.
