Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation

Understand

Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes.

• When deployed in an open world, the reliability of these models depends on their ability not only to classify in-distribution pixels but also to detect out-of-distribution (OoD) pixels.
• Historically, the poor OoD detection performance of these models has motivated the design of methods based on model re-training using synthetic training images that include OoD visual objects.
• Although successful, these re-trained methods have two issues: 1) their in-distribution segmentation accuracy may drop during re-training, and 2) their OoD detection accuracy does not generalise well to new contexts (e.g., country surroundings) outside the training set (e.g., city surroundings).