Papers › The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
Eu Wern Teh, Terrance DeVries, Brendan Duke, Ruowei Jiang, Parham Aarabi, Graham W. Taylor
We consider the task of semi-supervised semantic segmentation, where we aim to produce pixel-wise semantic object masks given only a small number of human-labeled training examples. We focus on iterative self-training methods in which we explore the behavior of self-training over multiple refinement stages. We show that iterative self-training leads to performance degradation if done na\"ively with a fixed ratio of human-labeled to pseudo-labeled training examples. We propose Greedy Iterative Self-Training (GIST) and Random Iterative Self-Training (RIST) strategies that alternate between training on either human-labeled data or pseudo-labeled data at each refinement stage, resulting in a performance boost rather than degradation. We further show that GIST and RIST can be combined with existing semi-supervised learning methods to boost performance.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Semantic Segmentation | Cityscapes 100 samples labeled | GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 58.70% | #8 of 13 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 12.5% labeled | GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 62.57% | #29 of 33 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 2% labeled | GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 53.51% | #1 of 3 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 25% labeled | GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 65.14% | #26 of 30 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 5% labeled | GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 59.98% | #1 of 3 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | GIST and RIST | Validation mIoU | 70.76% | #32 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 2% labeled | GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 67.21% | #3 of 12 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 5% labeled | GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained) | Validation mIoU | 69.40% | #7 of 14 | Archive leaderboard | report |
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