# Data Quality Aware Approaches for Addressing Model Drift of Semantic Segmentation Models

arXiv: https://arxiv.org/abs/2402.07258 · HTML: https://mlsanity.com/paper/2402.07258/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Person Re-identification in Aerial Imagery](https://mlsanity.com/paper/1908.05024/) (2019)
- [Detection of data drift and outliers affecting machine learning model performance over time](https://mlsanity.com/paper/2012.09258/) (2020)
- [Concept Drift Detection for Streaming Data](https://mlsanity.com/paper/1504.01044/) (2015)
- [Automatically detecting data drift in machine learning classifiers](https://mlsanity.com/paper/2111.05672/) (2021)
- [Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning](https://mlsanity.com/paper/2309.01656/) (2023)
- [Attention-based Shape and Gait Representations Learning for Video-based Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2402.03716/) (2024)

## Built on by (most cited first, 1 of 1)

- [M18K: A Comprehensive RGB-D Dataset and Benchmark for Mushroom Detection and Instance Segmentation](https://mlsanity.com/paper/2407.11275/) (2024)

## Abstract (arXiv)

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.