Realistic Predictors for Regression and Semantic Segmentation

• Published in: 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA) pp. 153 - 155
• Main Authors: Gadepally, Krishna Chaitanya, Bhusan Dhal, Sambandh, Kalafatis, Stavros, Nowka, Kevin J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23-05-2023
• Subjects: Computer vision; Hardness Predictor; Lower Variance; Noise; Noise measurement; Pipelines; Prediction algorithms; Regression; Regression analysis; Semantic segmentation; Training
• ISSN: 2770-8209
• DOI: 10.1109/SERA57763.2023.10197824

Computer vision and image processing algorithms work well under strong assumptions. Computer vision algorithms are not expected to do well on all kinds of inputs. For instance, excessively noisy images may not fetch optimal results for most computer vision algorithms. Unexpected outputs from the computer vision module can have negative downstream consequences for other modules in the pipeline. To mitigate such consequences, we use a predictor framework that was simultaneously trained with a Hardness Predictor network. This framework guarantees improved performance over those images with lower "hardness" values. The proposed predictor framework, when applied to the input data, would result in a relatively lower variance estimator when the size of the training set is large, both in the domain of semantic segmentation as well as regression analysis.