Semantic Self-adaptation: Enhancing Generalization with a Single Sample. (arXiv:2208.05788v1 [cs.CV])

Despite years of research, out-of-domain generalization remains 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. Self-adaptation operates on two
levels. First, it employs a self-supervised loss that customizes the parameters
of convolutional …

arxiv cv semantic