Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels Article Swipe
Yuchao Wang
,
Haochen Wang
,
Yujun Shen
,
Jingjing Fei
,
Wei Li
,
Guoqiang Jin
,
Liwei Wu
,
Rui Zhao
,
Xinyi Le
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.03884
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.03884
The crux of semi-supervised semantic segmentation is to assign adequate pseudo-labels to the pixels of unlabeled images. 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. We argue that every pixel matters to the model training, even its prediction is ambiguous. Intuitively, an unreliable prediction may get confused among the top classes (i.e., those with the highest probabilities), however, it should be confident about the pixel not belonging to the remaining classes. Hence, such a pixel can be convincingly treated as a negative sample to those most unlikely categories. Based on this insight, we develop an effective pipeline to make sufficient use of unlabeled data. Concretely, we separate reliable and unreliable pixels via the entropy of predictions, push each unreliable pixel to a category-wise queue that consists of negative samples, and manage to train the model with all candidate pixels. Considering the training evolution, where the prediction becomes more and more accurate, we adaptively adjust the threshold for the reliable-unreliable partition. Experimental results on various benchmarks and training settings demonstrate the superiority of our approach over the state-of-the-art alternatives.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2203.03884
- https://arxiv.org/pdf/2203.03884
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226367174
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4226367174Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2203.03884Digital Object Identifier
- Title
-
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-LabelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-08Full publication date if available
- Authors
-
Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, Xinyi LeList of authors in order
- Landing page
-
https://arxiv.org/abs/2203.03884Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2203.03884Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2203.03884Direct OA link when available
- Concepts
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Pixel, Segmentation, Computer science, Ground truth, Artificial intelligence, Partition (number theory), Pipeline (software), Entropy (arrow of time), Pattern recognition (psychology), Machine learning, Data mining, Mathematics, Programming language, Quantum mechanics, Physics, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Hence, | 94 |
| abstract_inverted_index.adjust | 176 |
| abstract_inverted_index.assign | 8 |
| abstract_inverted_index.common | 18 |
| abstract_inverted_index.highly | 24 |
| abstract_inverted_index.manage | 153 |
| abstract_inverted_index.pixels | 13, 39, 133 |
| abstract_inverted_index.pseudo | 29 |
| abstract_inverted_index.sample | 105 |
| abstract_inverted_index.select | 22 |
| abstract_inverted_index.should | 82 |
| abstract_inverted_index.unused | 43 |
| abstract_inverted_index.becomes | 169 |
| abstract_inverted_index.classes | 73 |
| abstract_inverted_index.develop | 116 |
| abstract_inverted_index.entropy | 136 |
| abstract_inverted_index.highest | 78 |
| abstract_inverted_index.images. | 16 |
| abstract_inverted_index.matters | 53 |
| abstract_inverted_index.pixels. | 161 |
| abstract_inverted_index.problem | 36 |
| abstract_inverted_index.results | 184 |
| abstract_inverted_index.treated | 101 |
| abstract_inverted_index.various | 186 |
| abstract_inverted_index.adequate | 9 |
| abstract_inverted_index.approach | 196 |
| abstract_inverted_index.classes. | 93 |
| abstract_inverted_index.confused | 69 |
| abstract_inverted_index.consists | 148 |
| abstract_inverted_index.however, | 80 |
| abstract_inverted_index.insight, | 114 |
| abstract_inverted_index.negative | 104, 150 |
| abstract_inverted_index.pipeline | 119 |
| abstract_inverted_index.practice | 19 |
| abstract_inverted_index.reliable | 130 |
| abstract_inverted_index.samples, | 151 |
| abstract_inverted_index.semantic | 4 |
| abstract_inverted_index.separate | 129 |
| abstract_inverted_index.settings | 190 |
| abstract_inverted_index.training | 164, 189 |
| abstract_inverted_index.unlikely | 109 |
| abstract_inverted_index.accurate, | 173 |
| abstract_inverted_index.belonging | 89 |
| abstract_inverted_index.candidate | 160 |
| abstract_inverted_index.confident | 25, 84 |
| abstract_inverted_index.effective | 118 |
| abstract_inverted_index.remaining | 92 |
| abstract_inverted_index.threshold | 178 |
| abstract_inverted_index.training, | 57 |
| abstract_inverted_index.unlabeled | 15, 125 |
| abstract_inverted_index.adaptively | 175 |
| abstract_inverted_index.ambiguous. | 62 |
| abstract_inverted_index.benchmarks | 187 |
| abstract_inverted_index.evolution, | 165 |
| abstract_inverted_index.partition. | 182 |
| abstract_inverted_index.prediction | 60, 66, 168 |
| abstract_inverted_index.sufficient | 122 |
| abstract_inverted_index.unreliable | 65, 132, 141 |
| abstract_inverted_index.Concretely, | 127 |
| abstract_inverted_index.Considering | 162 |
| abstract_inverted_index.categories. | 110 |
| abstract_inverted_index.demonstrate | 191 |
| abstract_inverted_index.predictions | 26 |
| abstract_inverted_index.superiority | 193 |
| abstract_inverted_index.Experimental | 183 |
| abstract_inverted_index.Intuitively, | 63 |
| abstract_inverted_index.convincingly | 100 |
| abstract_inverted_index.predictions, | 138 |
| abstract_inverted_index.segmentation | 5 |
| abstract_inverted_index.alternatives. | 200 |
| abstract_inverted_index.category-wise | 145 |
| abstract_inverted_index.ground-truth, | 30 |
| abstract_inverted_index.pseudo-labels | 10 |
| abstract_inverted_index.unreliability. | 47 |
| abstract_inverted_index.probabilities), | 79 |
| abstract_inverted_index.semi-supervised | 3 |
| abstract_inverted_index.state-of-the-art | 199 |
| abstract_inverted_index.reliable-unreliable | 181 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile |