Unsupervised Recurrent All-Pairs Field Transforms for Particle Image Velocimetry Article Swipe
Christian Lagemann
,
Michael Klaas
,
Wolfgang Schröder
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18409/ispiv.v1i1.120
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18409/ispiv.v1i1.120
Convolutional neural networks have been successfully used in a variety of tasks and recently have been adapted to improve processing steps in Particle-Image Velocimetry (PIV). Recurrent All-Pairs Fields Transforms (RAFT) as an optical flow estimation backbone achieve a new state-of-the-art accuracy on public synthetic PIV datasets, generalize well to unknown real-world experimental data, and allow a significantly higher spatial resolution compared to state-of-the-art PIV algorithms based on cross-correlation methods. However, the huge diversity in dynamic flows and varying particle image conditions require PIV processing schemes to have high generalization capabilities to unseen flow and lighting conditions. If these conditions vary strongly compared to the synthetic training data, the performance of fully supervised learning based PIV tools might degrade. To tackle these issues, our training procedure is augmented by an unsupervised learning paradigm which remedy the need of a general synthetic dataset and theoretically boosts the inference capability of a deep learning model in a way being more relevant to challenging real-world experimental data. Therefore, we propose URAFT-PIV, an unsupervised deep neural network architecture for optical flow estimation in PIV applications and show that our combination of state-of-the-art deep learning pipelines and unsupervised learning achieves a new state-of-the-art accuracy for unsupervised PIV networks while performing similar to supervisedly trained LiteFlowNet based competitors. Furthermore, we show that URAFT-PIV also performs well under more challenging flow field and image conditions such as low particle density and changing light conditions and demonstrate its generalization capability based on an outof-the-box application to real-world experimental data. Our tests also suggest that current state-of-the-art loss functions might be a limiting factor for the performance of unsupervised optical flow estimation.
Related Topics
Concepts
Artificial intelligence
Particle image velocimetry
Unsupervised learning
Computer science
Deep learning
Convolutional neural network
Optical flow
Field (mathematics)
Machine learning
Generalization
Artificial neural network
Pattern recognition (psychology)
Image (mathematics)
Mathematics
Physics
Mathematical analysis
Turbulence
Pure mathematics
Thermodynamics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18409/ispiv.v1i1.120
- https://ispiv21.library.iit.edu/index.php/ISPIV/article/download/120/125
- OA Status
- diamond
- Cited By
- 11
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3202863838
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3202863838Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18409/ispiv.v1i1.120Digital Object Identifier
- Title
-
Unsupervised Recurrent All-Pairs Field Transforms for Particle Image VelocimetryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-01Full publication date if available
- Authors
-
Christian Lagemann, Michael Klaas, Wolfgang SchröderList of authors in order
- Landing page
-
https://doi.org/10.18409/ispiv.v1i1.120Publisher landing page
- PDF URL
-
https://ispiv21.library.iit.edu/index.php/ISPIV/article/download/120/125Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://ispiv21.library.iit.edu/index.php/ISPIV/article/download/120/125Direct OA link when available
- Concepts
-
Artificial intelligence, Particle image velocimetry, Unsupervised learning, Computer science, Deep learning, Convolutional neural network, Optical flow, Field (mathematics), Machine learning, Generalization, Artificial neural network, Pattern recognition (psychology), Image (mathematics), Mathematics, Physics, Mathematical analysis, Turbulence, Pure mathematics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 7, 2023: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3202863838 |
|---|---|
| doi | https://doi.org/10.18409/ispiv.v1i1.120 |
| ids.doi | https://doi.org/10.18409/ispiv.v1i1.120 |
| ids.mag | 3202863838 |
| ids.openalex | https://openalex.org/W3202863838 |
| fwci | 1.76442247 |
| type | article |
| title | Unsupervised Recurrent All-Pairs Field Transforms for Particle Image Velocimetry |
| biblio.issue | 1 |
| biblio.volume | 1 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10360 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9958000183105469 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2206 |
| topics[0].subfield.display_name | Computational Mechanics |
| topics[0].display_name | Fluid Dynamics and Turbulent Flows |
| topics[1].id | https://openalex.org/T11105 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9764999747276306 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Advanced Image Processing Techniques |
| topics[2].id | https://openalex.org/T10266 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9480000138282776 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2306 |
| topics[2].subfield.display_name | Global and Planetary Change |
| topics[2].display_name | Plant Water Relations and Carbon Dynamics |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.7511740922927856 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C207857233 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7088122963905334 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1756248 |
| concepts[1].display_name | Particle image velocimetry |
| concepts[2].id | https://openalex.org/C8038995 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6998915672302246 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1152135 |
| concepts[2].display_name | Unsupervised learning |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6781772971153259 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C108583219 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6750712990760803 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[4].display_name | Deep learning |
| concepts[5].id | https://openalex.org/C81363708 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5419597625732422 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[5].display_name | Convolutional neural network |
| concepts[6].id | https://openalex.org/C155542232 |
| concepts[6].level | 3 |
| concepts[6].score | 0.5397859811782837 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q736111 |
| concepts[6].display_name | Optical flow |
| concepts[7].id | https://openalex.org/C9652623 |
| concepts[7].level | 2 |
| concepts[7].score | 0.48558253049850464 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q190109 |
| concepts[7].display_name | Field (mathematics) |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.46102413535118103 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C177148314 |
| concepts[9].level | 2 |
| concepts[9].score | 0.454832524061203 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q170084 |
| concepts[9].display_name | Generalization |
| concepts[10].id | https://openalex.org/C50644808 |
| concepts[10].level | 2 |
| concepts[10].score | 0.41734153032302856 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[10].display_name | Artificial neural network |
| concepts[11].id | https://openalex.org/C153180895 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4024029076099396 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[11].display_name | Pattern recognition (psychology) |
| concepts[12].id | https://openalex.org/C115961682 |
| concepts[12].level | 2 |
| concepts[12].score | 0.3040519952774048 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[12].display_name | Image (mathematics) |
| concepts[13].id | https://openalex.org/C33923547 |
| concepts[13].level | 0 |
| concepts[13].score | 0.1465023159980774 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[13].display_name | Mathematics |
| concepts[14].id | https://openalex.org/C121332964 |
| concepts[14].level | 0 |
| concepts[14].score | 0.069722980260849 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[14].display_name | Physics |
| concepts[15].id | https://openalex.org/C134306372 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[15].display_name | Mathematical analysis |
| concepts[16].id | https://openalex.org/C196558001 |
| concepts[16].level | 2 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q190132 |
| concepts[16].display_name | Turbulence |
| concepts[17].id | https://openalex.org/C202444582 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q837863 |
| concepts[17].display_name | Pure mathematics |
| concepts[18].id | https://openalex.org/C97355855 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[18].display_name | Thermodynamics |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.7511740922927856 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/particle-image-velocimetry |
| keywords[1].score | 0.7088122963905334 |
| keywords[1].display_name | Particle image velocimetry |
| keywords[2].id | https://openalex.org/keywords/unsupervised-learning |
| keywords[2].score | 0.6998915672302246 |
| keywords[2].display_name | Unsupervised learning |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6781772971153259 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/deep-learning |
| keywords[4].score | 0.6750712990760803 |
| keywords[4].display_name | Deep learning |
| keywords[5].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[5].score | 0.5419597625732422 |
| keywords[5].display_name | Convolutional neural network |
| keywords[6].id | https://openalex.org/keywords/optical-flow |
| keywords[6].score | 0.5397859811782837 |
| keywords[6].display_name | Optical flow |
| keywords[7].id | https://openalex.org/keywords/field |
| keywords[7].score | 0.48558253049850464 |
| keywords[7].display_name | Field (mathematics) |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.46102413535118103 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/generalization |
| keywords[9].score | 0.454832524061203 |
| keywords[9].display_name | Generalization |
| keywords[10].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[10].score | 0.41734153032302856 |
| keywords[10].display_name | Artificial neural network |
| keywords[11].id | https://openalex.org/keywords/pattern-recognition |
| keywords[11].score | 0.4024029076099396 |
| keywords[11].display_name | Pattern recognition (psychology) |
| keywords[12].id | https://openalex.org/keywords/image |
| keywords[12].score | 0.3040519952774048 |
| keywords[12].display_name | Image (mathematics) |
| keywords[13].id | https://openalex.org/keywords/mathematics |
| keywords[13].score | 0.1465023159980774 |
| keywords[13].display_name | Mathematics |
| keywords[14].id | https://openalex.org/keywords/physics |
| keywords[14].score | 0.069722980260849 |
| keywords[14].display_name | Physics |
| language | en |
| locations[0].id | doi:10.18409/ispiv.v1i1.120 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210208640 |
| locations[0].source.issn | 2769-7576 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2769-7576 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | 14th International Symposium on Particle Image Velocimetry |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | https://ispiv21.library.iit.edu/index.php/ISPIV/article/download/120/125 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | 14th International Symposium on Particle Image Velocimetry |
| locations[0].landing_page_url | https://doi.org/10.18409/ispiv.v1i1.120 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5085520597 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1150-4987 |
| authorships[0].author.display_name | Christian Lagemann |
| authorships[0].countries | DE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I887968799 |
| authorships[0].affiliations[0].raw_affiliation_string | Institute of Aerodynamics Aachen RWTH Aachen University, Germany |
| authorships[0].institutions[0].id | https://openalex.org/I887968799 |
| authorships[0].institutions[0].ror | https://ror.org/04xfq0f34 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I887968799 |
| authorships[0].institutions[0].country_code | DE |
| authorships[0].institutions[0].display_name | RWTH Aachen University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Christian Lagemann |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Institute of Aerodynamics Aachen RWTH Aachen University, Germany |
| authorships[1].author.id | https://openalex.org/A5010228512 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4263-3896 |
| authorships[1].author.display_name | Michael Klaas |
| authorships[1].countries | DE |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I887968799 |
| authorships[1].affiliations[0].raw_affiliation_string | Institute of Aerodynamics Aachen RWTH Aachen University, Germany |
| authorships[1].institutions[0].id | https://openalex.org/I887968799 |
| authorships[1].institutions[0].ror | https://ror.org/04xfq0f34 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I887968799 |
| authorships[1].institutions[0].country_code | DE |
| authorships[1].institutions[0].display_name | RWTH Aachen University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Michael Klaas |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Institute of Aerodynamics Aachen RWTH Aachen University, Germany |
| authorships[2].author.id | https://openalex.org/A5043578209 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3472-1813 |
| authorships[2].author.display_name | Wolfgang Schröder |
| authorships[2].countries | DE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I887968799 |
| authorships[2].affiliations[0].raw_affiliation_string | Institute of Aerodynamics Aachen RWTH Aachen University, Germany |
| authorships[2].institutions[0].id | https://openalex.org/I887968799 |
| authorships[2].institutions[0].ror | https://ror.org/04xfq0f34 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I887968799 |
| authorships[2].institutions[0].country_code | DE |
| authorships[2].institutions[0].display_name | RWTH Aachen University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Wolfgang Schröder |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Institute of Aerodynamics Aachen RWTH Aachen University, Germany |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://ispiv21.library.iit.edu/index.php/ISPIV/article/download/120/125 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Unsupervised Recurrent All-Pairs Field Transforms for Particle Image Velocimetry |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10360 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9958000183105469 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2206 |
| primary_topic.subfield.display_name | Computational Mechanics |
| primary_topic.display_name | Fluid Dynamics and Turbulent Flows |
| related_works | https://openalex.org/W3162204513, https://openalex.org/W4293226380, https://openalex.org/W2371138613, https://openalex.org/W2048963458, https://openalex.org/W43109613, https://openalex.org/W2359952343, https://openalex.org/W2080152487, https://openalex.org/W2239445980, https://openalex.org/W2762689969, https://openalex.org/W4282049427 |
| cited_by_count | 11 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 7 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| counts_by_year[3].year | 2021 |
| counts_by_year[3].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.18409/ispiv.v1i1.120 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210208640 |
| best_oa_location.source.issn | 2769-7576 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2769-7576 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | 14th International Symposium on Particle Image Velocimetry |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://ispiv21.library.iit.edu/index.php/ISPIV/article/download/120/125 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | 14th International Symposium on Particle Image Velocimetry |
| best_oa_location.landing_page_url | https://doi.org/10.18409/ispiv.v1i1.120 |
| primary_location.id | doi:10.18409/ispiv.v1i1.120 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210208640 |
| primary_location.source.issn | 2769-7576 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2769-7576 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | 14th International Symposium on Particle Image Velocimetry |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | https://ispiv21.library.iit.edu/index.php/ISPIV/article/download/120/125 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | 14th International Symposium on Particle Image Velocimetry |
| primary_location.landing_page_url | https://doi.org/10.18409/ispiv.v1i1.120 |
| publication_date | 2021-08-01 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2186289225, https://openalex.org/W4239021801, https://openalex.org/W2963891416, https://openalex.org/W3108086282, https://openalex.org/W1982865011, https://openalex.org/W2770660451, https://openalex.org/W1522301498, https://openalex.org/W2967686751, https://openalex.org/W2917818392, https://openalex.org/W1694289106, https://openalex.org/W3097424159, https://openalex.org/W2753953733, https://openalex.org/W2964156315, https://openalex.org/W764651262, https://openalex.org/W3120894323, https://openalex.org/W3184670886, https://openalex.org/W3109908659, https://openalex.org/W2153229054 |
| referenced_works_count | 18 |
| abstract_inverted_index.a | 8, 37, 55, 137, 148, 153, 194, 261 |
| abstract_inverted_index.If | 96 |
| abstract_inverted_index.To | 118 |
| abstract_inverted_index.an | 31, 128, 167, 243 |
| abstract_inverted_index.as | 30, 228 |
| abstract_inverted_index.be | 260 |
| abstract_inverted_index.by | 127 |
| abstract_inverted_index.in | 7, 21, 73, 152, 177 |
| abstract_inverted_index.is | 125 |
| abstract_inverted_index.of | 10, 109, 136, 147, 185, 267 |
| abstract_inverted_index.on | 41, 66, 242 |
| abstract_inverted_index.to | 17, 48, 61, 85, 90, 102, 158, 205, 246 |
| abstract_inverted_index.we | 164, 212 |
| abstract_inverted_index.Our | 250 |
| abstract_inverted_index.PIV | 44, 63, 82, 114, 178, 200 |
| abstract_inverted_index.and | 12, 53, 76, 93, 141, 180, 190, 224, 232, 236 |
| abstract_inverted_index.for | 173, 198, 264 |
| abstract_inverted_index.its | 238 |
| abstract_inverted_index.low | 229 |
| abstract_inverted_index.new | 38, 195 |
| abstract_inverted_index.our | 122, 183 |
| abstract_inverted_index.the | 70, 103, 107, 134, 144, 265 |
| abstract_inverted_index.way | 154 |
| abstract_inverted_index.also | 216, 252 |
| abstract_inverted_index.been | 4, 15 |
| abstract_inverted_index.deep | 149, 169, 187 |
| abstract_inverted_index.flow | 33, 92, 175, 222, 270 |
| abstract_inverted_index.have | 3, 14, 86 |
| abstract_inverted_index.high | 87 |
| abstract_inverted_index.huge | 71 |
| abstract_inverted_index.loss | 257 |
| abstract_inverted_index.more | 156, 220 |
| abstract_inverted_index.need | 135 |
| abstract_inverted_index.show | 181, 213 |
| abstract_inverted_index.such | 227 |
| abstract_inverted_index.that | 182, 214, 254 |
| abstract_inverted_index.used | 6 |
| abstract_inverted_index.vary | 99 |
| abstract_inverted_index.well | 47, 218 |
| abstract_inverted_index.allow | 54 |
| abstract_inverted_index.based | 65, 113, 209, 241 |
| abstract_inverted_index.being | 155 |
| abstract_inverted_index.data, | 52, 106 |
| abstract_inverted_index.data. | 162, 249 |
| abstract_inverted_index.field | 223 |
| abstract_inverted_index.flows | 75 |
| abstract_inverted_index.fully | 110 |
| abstract_inverted_index.image | 79, 225 |
| abstract_inverted_index.light | 234 |
| abstract_inverted_index.might | 116, 259 |
| abstract_inverted_index.model | 151 |
| abstract_inverted_index.steps | 20 |
| abstract_inverted_index.tasks | 11 |
| abstract_inverted_index.tests | 251 |
| abstract_inverted_index.these | 97, 120 |
| abstract_inverted_index.tools | 115 |
| abstract_inverted_index.under | 219 |
| abstract_inverted_index.which | 132 |
| abstract_inverted_index.while | 202 |
| abstract_inverted_index.(PIV). | 24 |
| abstract_inverted_index.(RAFT) | 29 |
| abstract_inverted_index.Fields | 27 |
| abstract_inverted_index.boosts | 143 |
| abstract_inverted_index.factor | 263 |
| abstract_inverted_index.higher | 57 |
| abstract_inverted_index.neural | 1, 170 |
| abstract_inverted_index.public | 42 |
| abstract_inverted_index.remedy | 133 |
| abstract_inverted_index.tackle | 119 |
| abstract_inverted_index.unseen | 91 |
| abstract_inverted_index.achieve | 36 |
| abstract_inverted_index.adapted | 16 |
| abstract_inverted_index.current | 255 |
| abstract_inverted_index.dataset | 140 |
| abstract_inverted_index.density | 231 |
| abstract_inverted_index.dynamic | 74 |
| abstract_inverted_index.general | 138 |
| abstract_inverted_index.improve | 18 |
| abstract_inverted_index.issues, | 121 |
| abstract_inverted_index.network | 171 |
| abstract_inverted_index.optical | 32, 174, 269 |
| abstract_inverted_index.propose | 165 |
| abstract_inverted_index.require | 81 |
| abstract_inverted_index.schemes | 84 |
| abstract_inverted_index.similar | 204 |
| abstract_inverted_index.spatial | 58 |
| abstract_inverted_index.suggest | 253 |
| abstract_inverted_index.trained | 207 |
| abstract_inverted_index.unknown | 49 |
| abstract_inverted_index.variety | 9 |
| abstract_inverted_index.varying | 77 |
| abstract_inverted_index.However, | 69 |
| abstract_inverted_index.accuracy | 40, 197 |
| abstract_inverted_index.achieves | 193 |
| abstract_inverted_index.backbone | 35 |
| abstract_inverted_index.changing | 233 |
| abstract_inverted_index.compared | 60, 101 |
| abstract_inverted_index.degrade. | 117 |
| abstract_inverted_index.learning | 112, 130, 150, 188, 192 |
| abstract_inverted_index.lighting | 94 |
| abstract_inverted_index.limiting | 262 |
| abstract_inverted_index.methods. | 68 |
| abstract_inverted_index.networks | 2, 201 |
| abstract_inverted_index.paradigm | 131 |
| abstract_inverted_index.particle | 78, 230 |
| abstract_inverted_index.performs | 217 |
| abstract_inverted_index.recently | 13 |
| abstract_inverted_index.relevant | 157 |
| abstract_inverted_index.strongly | 100 |
| abstract_inverted_index.training | 105, 123 |
| abstract_inverted_index.All-Pairs | 26 |
| abstract_inverted_index.Recurrent | 25 |
| abstract_inverted_index.URAFT-PIV | 215 |
| abstract_inverted_index.augmented | 126 |
| abstract_inverted_index.datasets, | 45 |
| abstract_inverted_index.diversity | 72 |
| abstract_inverted_index.functions | 258 |
| abstract_inverted_index.inference | 145 |
| abstract_inverted_index.pipelines | 189 |
| abstract_inverted_index.procedure | 124 |
| abstract_inverted_index.synthetic | 43, 104, 139 |
| abstract_inverted_index.Therefore, | 163 |
| abstract_inverted_index.Transforms | 28 |
| abstract_inverted_index.URAFT-PIV, | 166 |
| abstract_inverted_index.algorithms | 64 |
| abstract_inverted_index.capability | 146, 240 |
| abstract_inverted_index.conditions | 80, 98, 226, 235 |
| abstract_inverted_index.estimation | 34, 176 |
| abstract_inverted_index.generalize | 46 |
| abstract_inverted_index.performing | 203 |
| abstract_inverted_index.processing | 19, 83 |
| abstract_inverted_index.real-world | 50, 160, 247 |
| abstract_inverted_index.resolution | 59 |
| abstract_inverted_index.supervised | 111 |
| abstract_inverted_index.LiteFlowNet | 208 |
| abstract_inverted_index.Velocimetry | 23 |
| abstract_inverted_index.application | 245 |
| abstract_inverted_index.challenging | 159, 221 |
| abstract_inverted_index.combination | 184 |
| abstract_inverted_index.conditions. | 95 |
| abstract_inverted_index.demonstrate | 237 |
| abstract_inverted_index.estimation. | 271 |
| abstract_inverted_index.performance | 108, 266 |
| abstract_inverted_index.Furthermore, | 211 |
| abstract_inverted_index.applications | 179 |
| abstract_inverted_index.architecture | 172 |
| abstract_inverted_index.capabilities | 89 |
| abstract_inverted_index.competitors. | 210 |
| abstract_inverted_index.experimental | 51, 161, 248 |
| abstract_inverted_index.successfully | 5 |
| abstract_inverted_index.supervisedly | 206 |
| abstract_inverted_index.unsupervised | 129, 168, 191, 199, 268 |
| abstract_inverted_index.Convolutional | 0 |
| abstract_inverted_index.outof-the-box | 244 |
| abstract_inverted_index.significantly | 56 |
| abstract_inverted_index.theoretically | 142 |
| abstract_inverted_index.Particle-Image | 22 |
| abstract_inverted_index.generalization | 88, 239 |
| abstract_inverted_index.state-of-the-art | 39, 62, 186, 196, 256 |
| abstract_inverted_index.cross-correlation | 67 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.81764578 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |