HS-ResNet: Hierarchical-Split Block on Convolutional Neural Network Article Swipe
Pengcheng Yuan
,
Shufei Lin
,
Cheng Cui
,
Yuning Du
,
Ruoyu Guo
,
Dongliang He
,
Errui Ding
,
Shumin Han
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2010.07621
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2010.07621
This paper addresses representational block named Hierarchical-Split Block, which can be taken as a plug-and-play block to upgrade existing convolutional neural networks, improves model performance significantly in a network. Hierarchical-Split Block contains many hierarchical split and concatenate connections within one single residual block. We find multi-scale features is of great importance for numerous vision tasks. Moreover, Hierarchical-Split block is very flexible and efficient, which provides a large space of potential network architectures for different applications. In this work, we present a common backbone based on Hierarchical-Split block for tasks: image classification, object detection, instance segmentation and semantic image segmentation/parsing. Our approach shows significant improvements over all these core tasks in comparison with the baseline. As shown in Figure1, for image classification, our 50-layers network(HS-ResNet50) achieves 81.28% top-1 accuracy with competitive latency on ImageNet-1k dataset. It also outperforms most state-of-the-art models. The source code and models will be available on: https://github.com/PaddlePaddle/PaddleClas
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2010.07621
- https://arxiv.org/pdf/2010.07621
- OA Status
- green
- Cited By
- 35
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3093038272
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3093038272Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2010.07621Digital Object Identifier
- Title
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HS-ResNet: Hierarchical-Split Block on Convolutional Neural NetworkWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-10-15Full publication date if available
- Authors
-
Pengcheng Yuan, Shufei Lin, Cheng Cui, Yuning Du, Ruoyu Guo, Dongliang He, Errui Ding, Shumin HanList of authors in order
- Landing page
-
https://arxiv.org/abs/2010.07621Publisher landing page
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https://arxiv.org/pdf/2010.07621Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2010.07621Direct OA link when available
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Computer science, Block (permutation group theory), Artificial intelligence, Segmentation, Convolutional neural network, Pattern recognition (psychology), Parsing, Geometry, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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35Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 5, 2023: 13, 2022: 7, 2021: 9Per-year citation counts (last 5 years)
- References (count)
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41Number 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.approach | 100 |
| abstract_inverted_index.backbone | 82 |
| abstract_inverted_index.contains | 31 |
| abstract_inverted_index.dataset. | 133 |
| abstract_inverted_index.existing | 18 |
| abstract_inverted_index.features | 46 |
| abstract_inverted_index.flexible | 60 |
| abstract_inverted_index.improves | 22 |
| abstract_inverted_index.instance | 93 |
| abstract_inverted_index.network. | 28 |
| abstract_inverted_index.numerous | 52 |
| abstract_inverted_index.provides | 64 |
| abstract_inverted_index.residual | 41 |
| abstract_inverted_index.semantic | 96 |
| abstract_inverted_index.50-layers | 122 |
| abstract_inverted_index.Moreover, | 55 |
| abstract_inverted_index.addresses | 2 |
| abstract_inverted_index.available | 147 |
| abstract_inverted_index.baseline. | 113 |
| abstract_inverted_index.different | 73 |
| abstract_inverted_index.networks, | 21 |
| abstract_inverted_index.potential | 69 |
| abstract_inverted_index.comparison | 110 |
| abstract_inverted_index.detection, | 92 |
| abstract_inverted_index.efficient, | 62 |
| abstract_inverted_index.importance | 50 |
| abstract_inverted_index.ImageNet-1k | 132 |
| abstract_inverted_index.competitive | 129 |
| abstract_inverted_index.concatenate | 36 |
| abstract_inverted_index.connections | 37 |
| abstract_inverted_index.multi-scale | 45 |
| abstract_inverted_index.outperforms | 136 |
| abstract_inverted_index.performance | 24 |
| abstract_inverted_index.significant | 102 |
| abstract_inverted_index.hierarchical | 33 |
| abstract_inverted_index.improvements | 103 |
| abstract_inverted_index.segmentation | 94 |
| abstract_inverted_index.applications. | 74 |
| abstract_inverted_index.architectures | 71 |
| abstract_inverted_index.convolutional | 19 |
| abstract_inverted_index.plug-and-play | 14 |
| abstract_inverted_index.significantly | 25 |
| abstract_inverted_index.classification, | 90, 120 |
| abstract_inverted_index.representational | 3 |
| abstract_inverted_index.state-of-the-art | 138 |
| abstract_inverted_index.Hierarchical-Split | 6, 29, 56, 85 |
| abstract_inverted_index.network(HS-ResNet50) | 123 |
| abstract_inverted_index.segmentation/parsing. | 98 |
| abstract_inverted_index.https://github.com/PaddlePaddle/PaddleClas | 149 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |