Multi‐similarity based hyperrelation network for few‐shot segmentation Article Swipe
Xiangwen Shi
,
Shaobing Zhang
,
Miao Cheng
,
Liang‐Nian He
,
Zhe Cui
,
Xianghong Tang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1049/ipr2.12628
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1049/ipr2.12628
Few‐shot semantic segmentation aims at recognizing the object regions of unseen categories with only a few annotated examples as supervision. The key to few‐shot segmentation is to establish a robust semantic relationship between the support and query images and to prevent overfitting. In this paper, an effective multi‐similarity hyperrelation network (MSHNet) is proposed to tackle the few‐shot semantic segmentation problem. In MSHNet, a new generative prototype similarity (GPS) is proposed, which, together with cosine similarity, establishes a strong semantic relationship between supported images and query images. In addition, a symmetric merging block (SMB) in MSHNet is proposed to efficiently merge multi‐layer, multi‐shot, multi‐similarity features to generate hyperrelation features for semantic segmentation. Experimenting on two benchmark semantic segmentation datasets (Pascal − 5 i and COCO − 20 i ) shows that this method achieves a mean intersection‐over‐union score of 72.3% and 56.0%, respectively, which outperforms the state‐of‐the‐art methods by 1.9% and 6.5%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/ipr2.12628
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12628
- OA Status
- gold
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225760743
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4225760743Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/ipr2.12628Digital Object Identifier
- Title
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Multi‐similarity based hyperrelation network for few‐shot segmentationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-30Full publication date if available
- Authors
-
Xiangwen Shi, Shaobing Zhang, Miao Cheng, Liang‐Nian He, Zhe Cui, Xianghong TangList of authors in order
- Landing page
-
https://doi.org/10.1049/ipr2.12628Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12628Direct link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12628Direct OA link when available
- Concepts
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Computer science, Overfitting, Artificial intelligence, Segmentation, Pattern recognition (psychology), Cosine similarity, Feature (linguistics), Similarity (geometry), Semantic similarity, Image (mathematics), Artificial neural network, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.MSHNet, | 62 |
| abstract_inverted_index.between | 33, 81 |
| abstract_inverted_index.images. | 86 |
| abstract_inverted_index.merging | 91 |
| abstract_inverted_index.methods | 147 |
| abstract_inverted_index.network | 50 |
| abstract_inverted_index.prevent | 41 |
| abstract_inverted_index.regions | 9 |
| abstract_inverted_index.support | 35 |
| abstract_inverted_index.(MSHNet) | 51 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.achieves | 133 |
| abstract_inverted_index.datasets | 118 |
| abstract_inverted_index.examples | 18 |
| abstract_inverted_index.features | 104, 108 |
| abstract_inverted_index.generate | 106 |
| abstract_inverted_index.problem. | 60 |
| abstract_inverted_index.proposed | 53, 97 |
| abstract_inverted_index.semantic | 2, 31, 58, 79, 110, 116 |
| abstract_inverted_index.together | 72 |
| abstract_inverted_index.addition, | 88 |
| abstract_inverted_index.annotated | 17 |
| abstract_inverted_index.benchmark | 115 |
| abstract_inverted_index.effective | 47 |
| abstract_inverted_index.establish | 28 |
| abstract_inverted_index.proposed, | 70 |
| abstract_inverted_index.prototype | 66 |
| abstract_inverted_index.supported | 82 |
| abstract_inverted_index.symmetric | 90 |
| abstract_inverted_index.Few‐shot | 1 |
| abstract_inverted_index.categories | 12 |
| abstract_inverted_index.few‐shot | 24, 57 |
| abstract_inverted_index.generative | 65 |
| abstract_inverted_index.similarity | 67 |
| abstract_inverted_index.efficiently | 99 |
| abstract_inverted_index.establishes | 76 |
| abstract_inverted_index.outperforms | 144 |
| abstract_inverted_index.recognizing | 6 |
| abstract_inverted_index.similarity, | 75 |
| abstract_inverted_index.overfitting. | 42 |
| abstract_inverted_index.relationship | 32, 80 |
| abstract_inverted_index.segmentation | 3, 25, 59, 117 |
| abstract_inverted_index.supervision. | 20 |
| abstract_inverted_index.Experimenting | 112 |
| abstract_inverted_index.hyperrelation | 49, 107 |
| abstract_inverted_index.multi‐shot, | 102 |
| abstract_inverted_index.respectively, | 142 |
| abstract_inverted_index.segmentation. | 111 |
| abstract_inverted_index.multi‐layer, | 101 |
| abstract_inverted_index.multi‐similarity | 48, 103 |
| abstract_inverted_index.state‐of‐the‐art | 146 |
| abstract_inverted_index.intersection‐over‐union | 136 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.15974004 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |