MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation

Understand

In semantic segmentation datasets, classes of high importance are oftentimes underrepresented, e.g., humans in street scenes.

• Neural networks are usually trained to reduce the overall number of errors, attaching identical loss to errors of all kinds.
• However, this is not necessarily aligned with human intuition.
• For instance, an overlooked pedestrian seems more severe than an incorrectly detected one.