An Unbiased Feature Estimation Network for Few-Shot Fine-Grained Image Classification Article Swipe
Jiale Wang
,
Jin Lu
,
Junpo Yang
,
Meijia Wang
,
Weichuan Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24237737
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24237737
Few-shot fine-grained image classification (FSFGIC) aims to classify subspecies with similar appearances under conditions of very limited data. In this paper, we observe an interesting phenomenon: different types of image data augmentation techniques have varying effects on the performance of FSFGIC methods. This indicates that there may be biases in the features extracted from the input images. The bias of the acquired feature may cause deviation in the calculation of similarity, which is particularly detrimental to FSFGIC tasks characterized by low inter-class variation and high intra-class variation, thus affecting the classification accuracy. To address the problems mentioned, we propose an unbiased feature estimation network. The designed network has the capability to significantly optimize the quality of the obtained feature representations and effectively reduce the feature bias from input images. Furthermore, our proposed architecture can be easily integrated into any contextual training mechanism. Extensive experiments on the FSFGIC tasks demonstrate the effectiveness of the proposed algorithm, showing a notable improvement in classification accuracy.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24237737
- OA Status
- gold
- Cited By
- 2
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405001139
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405001139Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s24237737Digital Object Identifier
- Title
-
An Unbiased Feature Estimation Network for Few-Shot Fine-Grained Image ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-03Full publication date if available
- Authors
-
Jiale Wang, Jin Lu, Junpo Yang, Meijia Wang, Weichuan ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/s24237737Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/s24237737Direct OA link when available
- Concepts
-
Computer science, Feature (linguistics), Pattern recognition (psychology), Artificial intelligence, Variation (astronomy), Class (philosophy), Artificial neural network, Image (mathematics), Similarity (geometry), Feature extraction, Data mining, Physics, Astrophysics, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
51Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4405001139 |
|---|---|
| doi | https://doi.org/10.3390/s24237737 |
| ids.doi | https://doi.org/10.3390/s24237737 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39686274 |
| ids.openalex | https://openalex.org/W4405001139 |
| fwci | 1.2775571 |
| type | article |
| title | An Unbiased Feature Estimation Network for Few-Shot Fine-Grained Image Classification |
| biblio.issue | 23 |
| biblio.volume | 24 |
| biblio.last_page | 7737 |
| biblio.first_page | 7737 |
| topics[0].id | https://openalex.org/T11307 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9983000159263611 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Domain Adaptation and Few-Shot Learning |
| topics[1].id | https://openalex.org/T10627 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9757000207901001 |
| 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 and Video Retrieval Techniques |
| topics[2].id | https://openalex.org/T11970 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9745000004768372 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | Molecular Biology Techniques and Applications |
| is_xpac | False |
| apc_list.value | 2400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2598 |
| apc_paid.value | 2400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2598 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7226560711860657 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C2776401178 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7020114064216614 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[1].display_name | Feature (linguistics) |
| concepts[2].id | https://openalex.org/C153180895 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6689831018447876 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[2].display_name | Pattern recognition (psychology) |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6271458864212036 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C2778334786 |
| concepts[4].level | 2 |
| concepts[4].score | 0.545211672782898 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1586270 |
| concepts[4].display_name | Variation (astronomy) |
| concepts[5].id | https://openalex.org/C2777212361 |
| concepts[5].level | 2 |
| concepts[5].score | 0.48876261711120605 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5127848 |
| concepts[5].display_name | Class (philosophy) |
| concepts[6].id | https://openalex.org/C50644808 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4682287573814392 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[6].display_name | Artificial neural network |
| concepts[7].id | https://openalex.org/C115961682 |
| concepts[7].level | 2 |
| concepts[7].score | 0.46740394830703735 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[7].display_name | Image (mathematics) |
| concepts[8].id | https://openalex.org/C103278499 |
| concepts[8].level | 3 |
| concepts[8].score | 0.45344406366348267 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q254465 |
| concepts[8].display_name | Similarity (geometry) |
| concepts[9].id | https://openalex.org/C52622490 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4277942180633545 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1026626 |
| concepts[9].display_name | Feature extraction |
| concepts[10].id | https://openalex.org/C124101348 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3782040476799011 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[10].display_name | Data mining |
| concepts[11].id | https://openalex.org/C121332964 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[11].display_name | Physics |
| concepts[12].id | https://openalex.org/C44870925 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q37547 |
| concepts[12].display_name | Astrophysics |
| concepts[13].id | https://openalex.org/C41895202 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[13].display_name | Linguistics |
| concepts[14].id | https://openalex.org/C138885662 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[14].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7226560711860657 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/feature |
| keywords[1].score | 0.7020114064216614 |
| keywords[1].display_name | Feature (linguistics) |
| keywords[2].id | https://openalex.org/keywords/pattern-recognition |
| keywords[2].score | 0.6689831018447876 |
| keywords[2].display_name | Pattern recognition (psychology) |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6271458864212036 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/variation |
| keywords[4].score | 0.545211672782898 |
| keywords[4].display_name | Variation (astronomy) |
| keywords[5].id | https://openalex.org/keywords/class |
| keywords[5].score | 0.48876261711120605 |
| keywords[5].display_name | Class (philosophy) |
| keywords[6].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[6].score | 0.4682287573814392 |
| keywords[6].display_name | Artificial neural network |
| keywords[7].id | https://openalex.org/keywords/image |
| keywords[7].score | 0.46740394830703735 |
| keywords[7].display_name | Image (mathematics) |
| keywords[8].id | https://openalex.org/keywords/similarity |
| keywords[8].score | 0.45344406366348267 |
| keywords[8].display_name | Similarity (geometry) |
| keywords[9].id | https://openalex.org/keywords/feature-extraction |
| keywords[9].score | 0.4277942180633545 |
| keywords[9].display_name | Feature extraction |
| keywords[10].id | https://openalex.org/keywords/data-mining |
| keywords[10].score | 0.3782040476799011 |
| keywords[10].display_name | Data mining |
| language | en |
| locations[0].id | doi:10.3390/s24237737 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S101949793 |
| locations[0].source.issn | 1424-8220 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1424-8220 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Sensors |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Sensors |
| locations[0].landing_page_url | https://doi.org/10.3390/s24237737 |
| locations[1].id | pmid:39686274 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Sensors (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39686274 |
| locations[2].id | pmh:oai:doaj.org/article:e52c8695456749228e27df17338a45b7 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].source.host_organization_lineage | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors, Vol 24, Iss 23, p 7737 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/e52c8695456749228e27df17338a45b7 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5100375445 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Jiale Wang |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I51622183 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China |
| authorships[0].institutions[0].id | https://openalex.org/I51622183 |
| authorships[0].institutions[0].ror | https://ror.org/034t3zs45 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I51622183 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Shaanxi University of Science and Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jiale Wang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China |
| authorships[1].author.id | https://openalex.org/A5114734497 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5964-9272 |
| authorships[1].author.display_name | Jin Lu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I51622183 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China |
| authorships[1].institutions[0].id | https://openalex.org/I51622183 |
| authorships[1].institutions[0].ror | https://ror.org/034t3zs45 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I51622183 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Shaanxi University of Science and Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jin Lu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China |
| authorships[2].author.id | https://openalex.org/A5026686074 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0449-6333 |
| authorships[2].author.display_name | Junpo Yang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I51622183 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China |
| authorships[2].institutions[0].id | https://openalex.org/I51622183 |
| authorships[2].institutions[0].ror | https://ror.org/034t3zs45 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I51622183 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Shaanxi University of Science and Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Junpo Yang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China |
| authorships[3].author.id | https://openalex.org/A5034345016 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3596-1229 |
| authorships[3].author.display_name | Meijia Wang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I51622183 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China |
| authorships[3].institutions[0].id | https://openalex.org/I51622183 |
| authorships[3].institutions[0].ror | https://ror.org/034t3zs45 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I51622183 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Shaanxi University of Science and Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Meijia Wang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China |
| authorships[4].author.id | https://openalex.org/A5013741704 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-4904-1826 |
| authorships[4].author.display_name | Weichuan Zhang |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I51622183 |
| authorships[4].affiliations[0].raw_affiliation_string | School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China |
| authorships[4].institutions[0].id | https://openalex.org/I51622183 |
| authorships[4].institutions[0].ror | https://ror.org/034t3zs45 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I51622183 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Shaanxi University of Science and Technology |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Weichuan Zhang |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710000, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3390/s24237737 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | An Unbiased Feature Estimation Network for Few-Shot Fine-Grained Image Classification |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11307 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9983000159263611 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Domain Adaptation and Few-Shot Learning |
| related_works | https://openalex.org/W2386430105, https://openalex.org/W2356521405, https://openalex.org/W2038534795, https://openalex.org/W2384358604, https://openalex.org/W1567829292, https://openalex.org/W3001063351, https://openalex.org/W3196905815, https://openalex.org/W2351370765, https://openalex.org/W2556800355, https://openalex.org/W2348490172 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/s24237737 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S101949793 |
| best_oa_location.source.issn | 1424-8220 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1424-8220 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Sensors |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Sensors |
| best_oa_location.landing_page_url | https://doi.org/10.3390/s24237737 |
| primary_location.id | doi:10.3390/s24237737 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101949793 |
| primary_location.source.issn | 1424-8220 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1424-8220 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Sensors |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sensors |
| primary_location.landing_page_url | https://doi.org/10.3390/s24237737 |
| publication_date | 2024-12-03 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4392507002, https://openalex.org/W4382462216, https://openalex.org/W2957105406, https://openalex.org/W3009864380, https://openalex.org/W4285299012, https://openalex.org/W4389060294, https://openalex.org/W3201987584, https://openalex.org/W4223994295, https://openalex.org/W3030401348, https://openalex.org/W4396985553, https://openalex.org/W4399565922, https://openalex.org/W4402573270, https://openalex.org/W2194775991, https://openalex.org/W6735236233, https://openalex.org/W3035102141, https://openalex.org/W2979689312, https://openalex.org/W3091449283, https://openalex.org/W3034978645, https://openalex.org/W2882999943, https://openalex.org/W3107763055, https://openalex.org/W4388819709, https://openalex.org/W3170373133, https://openalex.org/W4283781662, https://openalex.org/W4318148776, https://openalex.org/W3176341011, https://openalex.org/W2058062991, https://openalex.org/W4399906778, https://openalex.org/W4303982493, https://openalex.org/W2138011018, https://openalex.org/W4280538209, https://openalex.org/W4295015045, https://openalex.org/W4283834642, https://openalex.org/W4283802957, https://openalex.org/W4312500832, https://openalex.org/W4316660800, https://openalex.org/W4393148571, https://openalex.org/W2965572487, https://openalex.org/W6777150884, https://openalex.org/W2143668817, https://openalex.org/W4312935555, https://openalex.org/W4394015587, https://openalex.org/W4376464610, https://openalex.org/W6717697761, https://openalex.org/W2618530766, https://openalex.org/W114517082, https://openalex.org/W3035143213, https://openalex.org/W4304098587, https://openalex.org/W2962858109, https://openalex.org/W4307340220, https://openalex.org/W3034827765, https://openalex.org/W2963341924 |
| referenced_works_count | 51 |
| abstract_inverted_index.a | 156 |
| abstract_inverted_index.In | 18 |
| abstract_inverted_index.To | 92 |
| abstract_inverted_index.an | 23, 99 |
| abstract_inverted_index.be | 47, 134 |
| abstract_inverted_index.by | 79 |
| abstract_inverted_index.in | 49, 66, 159 |
| abstract_inverted_index.is | 72 |
| abstract_inverted_index.of | 14, 28, 39, 59, 69, 115, 151 |
| abstract_inverted_index.on | 36, 144 |
| abstract_inverted_index.to | 6, 75, 110 |
| abstract_inverted_index.we | 21, 97 |
| abstract_inverted_index.The | 57, 104 |
| abstract_inverted_index.and | 83, 120 |
| abstract_inverted_index.any | 138 |
| abstract_inverted_index.can | 133 |
| abstract_inverted_index.has | 107 |
| abstract_inverted_index.low | 80 |
| abstract_inverted_index.may | 46, 63 |
| abstract_inverted_index.our | 130 |
| abstract_inverted_index.the | 37, 50, 54, 60, 67, 89, 94, 108, 113, 116, 123, 145, 149, 152 |
| abstract_inverted_index.This | 42 |
| abstract_inverted_index.aims | 5 |
| abstract_inverted_index.bias | 58, 125 |
| abstract_inverted_index.data | 30 |
| abstract_inverted_index.from | 53, 126 |
| abstract_inverted_index.have | 33 |
| abstract_inverted_index.high | 84 |
| abstract_inverted_index.into | 137 |
| abstract_inverted_index.that | 44 |
| abstract_inverted_index.this | 19 |
| abstract_inverted_index.thus | 87 |
| abstract_inverted_index.very | 15 |
| abstract_inverted_index.with | 9 |
| abstract_inverted_index.cause | 64 |
| abstract_inverted_index.data. | 17 |
| abstract_inverted_index.image | 2, 29 |
| abstract_inverted_index.input | 55, 127 |
| abstract_inverted_index.tasks | 77, 147 |
| abstract_inverted_index.there | 45 |
| abstract_inverted_index.types | 27 |
| abstract_inverted_index.under | 12 |
| abstract_inverted_index.which | 71 |
| abstract_inverted_index.FSFGIC | 40, 76, 146 |
| abstract_inverted_index.biases | 48 |
| abstract_inverted_index.easily | 135 |
| abstract_inverted_index.paper, | 20 |
| abstract_inverted_index.reduce | 122 |
| abstract_inverted_index.address | 93 |
| abstract_inverted_index.effects | 35 |
| abstract_inverted_index.feature | 62, 101, 118, 124 |
| abstract_inverted_index.images. | 56, 128 |
| abstract_inverted_index.limited | 16 |
| abstract_inverted_index.network | 106 |
| abstract_inverted_index.notable | 157 |
| abstract_inverted_index.observe | 22 |
| abstract_inverted_index.propose | 98 |
| abstract_inverted_index.quality | 114 |
| abstract_inverted_index.showing | 155 |
| abstract_inverted_index.similar | 10 |
| abstract_inverted_index.varying | 34 |
| abstract_inverted_index.(FSFGIC) | 4 |
| abstract_inverted_index.Few-shot | 0 |
| abstract_inverted_index.acquired | 61 |
| abstract_inverted_index.classify | 7 |
| abstract_inverted_index.designed | 105 |
| abstract_inverted_index.features | 51 |
| abstract_inverted_index.methods. | 41 |
| abstract_inverted_index.network. | 103 |
| abstract_inverted_index.obtained | 117 |
| abstract_inverted_index.optimize | 112 |
| abstract_inverted_index.problems | 95 |
| abstract_inverted_index.proposed | 131, 153 |
| abstract_inverted_index.training | 140 |
| abstract_inverted_index.unbiased | 100 |
| abstract_inverted_index.Extensive | 142 |
| abstract_inverted_index.accuracy. | 91, 161 |
| abstract_inverted_index.affecting | 88 |
| abstract_inverted_index.deviation | 65 |
| abstract_inverted_index.different | 26 |
| abstract_inverted_index.extracted | 52 |
| abstract_inverted_index.indicates | 43 |
| abstract_inverted_index.variation | 82 |
| abstract_inverted_index.algorithm, | 154 |
| abstract_inverted_index.capability | 109 |
| abstract_inverted_index.conditions | 13 |
| abstract_inverted_index.contextual | 139 |
| abstract_inverted_index.estimation | 102 |
| abstract_inverted_index.integrated | 136 |
| abstract_inverted_index.mechanism. | 141 |
| abstract_inverted_index.mentioned, | 96 |
| abstract_inverted_index.subspecies | 8 |
| abstract_inverted_index.techniques | 32 |
| abstract_inverted_index.variation, | 86 |
| abstract_inverted_index.appearances | 11 |
| abstract_inverted_index.calculation | 68 |
| abstract_inverted_index.demonstrate | 148 |
| abstract_inverted_index.detrimental | 74 |
| abstract_inverted_index.effectively | 121 |
| abstract_inverted_index.experiments | 143 |
| abstract_inverted_index.improvement | 158 |
| abstract_inverted_index.inter-class | 81 |
| abstract_inverted_index.interesting | 24 |
| abstract_inverted_index.intra-class | 85 |
| abstract_inverted_index.performance | 38 |
| abstract_inverted_index.phenomenon: | 25 |
| abstract_inverted_index.similarity, | 70 |
| abstract_inverted_index.Furthermore, | 129 |
| abstract_inverted_index.architecture | 132 |
| abstract_inverted_index.augmentation | 31 |
| abstract_inverted_index.fine-grained | 1 |
| abstract_inverted_index.particularly | 73 |
| abstract_inverted_index.characterized | 78 |
| abstract_inverted_index.effectiveness | 150 |
| abstract_inverted_index.significantly | 111 |
| abstract_inverted_index.classification | 3, 90, 160 |
| abstract_inverted_index.representations | 119 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.81772978 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |