  Recently, semi-supervised semantic segmentation has achieved promising
performance with a small fraction of labeled data. However, most existing
studies treat all unlabeled data equally and barely consider the differences
and training difficulties among unlabeled instances. Differentiating unlabeled
instances can promote instance-specific supervision to adapt to the model's
evolution dynamically. In this paper, we emphasize the cruciality of instance
differences and propose an instance-specific and model-adaptive supervision for
semi-supervised semantic segmentation, named iMAS. Relying on the model's
performance, iMAS employs a class-weighted symmetric intersection-over-union to
evaluate quantitative hardness of each unlabeled instance and supervises the
training on unlabeled data in a model-adaptive manner. Specifically, iMAS
learns from unlabeled instances progressively by weighing their corresponding
consistency losses based on the evaluated hardness. Besides, iMAS dynamically
adjusts the augmentation for each instance such that the distortion degree of
augmented instances is adapted to the model's generalization capability across
the training course. Not integrating additional losses and training procedures,
iMAS can obtain remarkable performance gains against current state-of-the-art
approaches on segmentation benchmarks under different semi-supervised partition
protocols.
