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Found 3 papers, 2 papers with code
Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation
A common practice is to select the highly confident predictions as the pseudo-ground-truths for each pixel, but it leads to a problem that most pixels may be left unused due to their unreliability.
Balancing Logit Variation for Long-tailed Semantic Segmentation
In this way, we manage to close the gap between the feature areas of different categories, resulting in a more balanced representation.
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
A common practice is to select the highly confident predictions as the pseudo ground-truth, but it leads to a problem that most pixels may be left unused due to their unreliability.
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