Semantic Self-adaptation: Enhancing Generalization with a Single Sample

Understand

The lack of out-of-domain generalization is a critical weakness of deep networks for semantic segmentation.

• Previous studies relied on the assumption of a static model, i.
• e., once the training process is complete, model parameters remain fixed at test time.
• In this work, we challenge this premise with a self-adaptive approach for semantic segmentation that adjusts the inference process to each input sample.