Optimizing Continuous Prompts for Visual Relationship Detection by Affix-Tuning Article Swipe
Shouguan Xiao
,
Weiping Fu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3187263
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/access.2022.3187263
Visual relationship detection is crucial for understanding visual scenes and is widely used in many areas, including visual navigation, visual question answering, and machine trouble detection. Traditional detection methods often fuse multiple region modules, which takes considerable time and resources to train every module with extensive samples. As every module is independent, the computation process has difficulty achieving unity and lacks a higher level of logical reasonability. In response to the above problems, we propose a novel method of affix-tuning transformers for visual relationship detection tasks, which keeps transformer model parameters frozen and optimizes a small continuous task-specific vector. It not only makes the model unified and reduces the training cost but also maintains the common-sense reasonability without multiscale training. In addition, we design a vision-and-language sentence expression prompt template and train a few transformer model parameters for downstream tasks. Our method, Prompt Template and Affix-Tuning Transformers (PTAT), is evaluated on visual relationship detection and Visual Genome datasets. Finally, the results of the proposed method are close to or even higher than those of the state-of-the-art methods on some evaluation metrics.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2022.3187263
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/09815128.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285307397
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4285307397Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2022.3187263Digital Object Identifier
- Title
-
Optimizing Continuous Prompts for Visual Relationship Detection by Affix-TuningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Shouguan Xiao, Weiping FuList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2022.3187263Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/09815128.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/09815128.pdfDirect OA link when available
- Concepts
-
Computer science, Transformer, Artificial intelligence, Sentence, Affix, Visualization, Fuse (electrical), Computation, Pattern recognition (psychology), Machine learning, Voltage, Algorithm, Quantum mechanics, Engineering, Electrical engineering, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4285307397 |
|---|---|
| doi | https://doi.org/10.1109/access.2022.3187263 |
| ids.doi | https://doi.org/10.1109/access.2022.3187263 |
| ids.openalex | https://openalex.org/W4285307397 |
| fwci | 0.1237918 |
| type | article |
| title | Optimizing Continuous Prompts for Visual Relationship Detection by Affix-Tuning |
| biblio.issue | |
| biblio.volume | 10 |
| biblio.last_page | 70112 |
| biblio.first_page | 70104 |
| topics[0].id | https://openalex.org/T11714 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Multimodal Machine Learning Applications |
| 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 | 0.9987000226974487 |
| 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/T11439 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.991599977016449 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Video Analysis and Summarization |
| is_xpac | False |
| apc_list.value | 1850 |
| apc_list.currency | USD |
| apc_list.value_usd | 1850 |
| apc_paid.value | 1850 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1850 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8385197520256042 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C66322947 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7233947515487671 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11658 |
| concepts[1].display_name | Transformer |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6264587640762329 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C2777530160 |
| concepts[3].level | 2 |
| concepts[3].score | 0.47673308849334717 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q41796 |
| concepts[3].display_name | Sentence |
| concepts[4].id | https://openalex.org/C2778428490 |
| concepts[4].level | 2 |
| concepts[4].score | 0.47487327456474304 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q62155 |
| concepts[4].display_name | Affix |
| concepts[5].id | https://openalex.org/C36464697 |
| concepts[5].level | 2 |
| concepts[5].score | 0.472329318523407 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q451553 |
| concepts[5].display_name | Visualization |
| concepts[6].id | https://openalex.org/C141353440 |
| concepts[6].level | 2 |
| concepts[6].score | 0.46690207719802856 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q182221 |
| concepts[6].display_name | Fuse (electrical) |
| concepts[7].id | https://openalex.org/C45374587 |
| concepts[7].level | 2 |
| concepts[7].score | 0.44834890961647034 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12525525 |
| concepts[7].display_name | Computation |
| concepts[8].id | https://openalex.org/C153180895 |
| concepts[8].level | 2 |
| concepts[8].score | 0.40070629119873047 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[8].display_name | Pattern recognition (psychology) |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3891071379184723 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C165801399 |
| concepts[10].level | 2 |
| concepts[10].score | 0.13253352046012878 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q25428 |
| concepts[10].display_name | Voltage |
| concepts[11].id | https://openalex.org/C11413529 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0882544219493866 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[11].display_name | Algorithm |
| concepts[12].id | https://openalex.org/C62520636 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[12].display_name | Quantum mechanics |
| concepts[13].id | https://openalex.org/C127413603 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[13].display_name | Engineering |
| concepts[14].id | https://openalex.org/C119599485 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[14].display_name | Electrical engineering |
| concepts[15].id | https://openalex.org/C121332964 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[15].display_name | Physics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8385197520256042 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/transformer |
| keywords[1].score | 0.7233947515487671 |
| keywords[1].display_name | Transformer |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.6264587640762329 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/sentence |
| keywords[3].score | 0.47673308849334717 |
| keywords[3].display_name | Sentence |
| keywords[4].id | https://openalex.org/keywords/affix |
| keywords[4].score | 0.47487327456474304 |
| keywords[4].display_name | Affix |
| keywords[5].id | https://openalex.org/keywords/visualization |
| keywords[5].score | 0.472329318523407 |
| keywords[5].display_name | Visualization |
| keywords[6].id | https://openalex.org/keywords/fuse |
| keywords[6].score | 0.46690207719802856 |
| keywords[6].display_name | Fuse (electrical) |
| keywords[7].id | https://openalex.org/keywords/computation |
| keywords[7].score | 0.44834890961647034 |
| keywords[7].display_name | Computation |
| keywords[8].id | https://openalex.org/keywords/pattern-recognition |
| keywords[8].score | 0.40070629119873047 |
| keywords[8].display_name | Pattern recognition (psychology) |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.3891071379184723 |
| keywords[9].display_name | Machine learning |
| keywords[10].id | https://openalex.org/keywords/voltage |
| keywords[10].score | 0.13253352046012878 |
| keywords[10].display_name | Voltage |
| keywords[11].id | https://openalex.org/keywords/algorithm |
| keywords[11].score | 0.0882544219493866 |
| keywords[11].display_name | Algorithm |
| language | en |
| locations[0].id | doi:10.1109/access.2022.3187263 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2485537415 |
| locations[0].source.issn | 2169-3536 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2169-3536 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | IEEE Access |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/6514899/09815128.pdf |
| 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 | IEEE Access |
| locations[0].landing_page_url | https://doi.org/10.1109/access.2022.3187263 |
| locations[1].id | pmh:oai:doaj.org/article:1e7fa7edb9554336ab561fd13eca3119 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | IEEE Access, Vol 10, Pp 70104-70112 (2022) |
| locations[1].landing_page_url | https://doaj.org/article/1e7fa7edb9554336ab561fd13eca3119 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5027436841 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-7169-8114 |
| authorships[0].author.display_name | Shouguan Xiao |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210131919 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210131919 |
| authorships[0].institutions[0].ror | https://ror.org/038avdt50 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210131919 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Xi'an University of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Shouguan Xiao |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, China |
| authorships[1].author.id | https://openalex.org/A5101938303 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-9803-3908 |
| authorships[1].author.display_name | Weiping Fu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210129906 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Engineering, Xi'an International University, Xi'an 710077, China |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I4210131919 |
| authorships[1].affiliations[1].raw_affiliation_string | School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, China |
| authorships[1].institutions[0].id | https://openalex.org/I4210131919 |
| authorships[1].institutions[0].ror | https://ror.org/038avdt50 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210131919 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Xi'an University of Technology |
| authorships[1].institutions[1].id | https://openalex.org/I4210129906 |
| authorships[1].institutions[1].ror | https://ror.org/02w30qy89 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I4210129906 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | Xi’an International University |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Weiping Fu |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | School of Engineering, Xi'an International University, Xi'an 710077, China, School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://ieeexplore.ieee.org/ielx7/6287639/6514899/09815128.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Optimizing Continuous Prompts for Visual Relationship Detection by Affix-Tuning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11714 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Multimodal Machine Learning Applications |
| related_works | https://openalex.org/W2057740391, https://openalex.org/W3112689701, https://openalex.org/W2366359738, https://openalex.org/W2386747520, https://openalex.org/W2349705404, https://openalex.org/W2354194029, https://openalex.org/W2373354203, https://openalex.org/W2350486244, https://openalex.org/W2347227047, https://openalex.org/W2366484208 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2022 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1109/access.2022.3187263 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2485537415 |
| best_oa_location.source.issn | 2169-3536 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2169-3536 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | IEEE Access |
| best_oa_location.source.host_organization | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/6514899/09815128.pdf |
| 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 | IEEE Access |
| best_oa_location.landing_page_url | https://doi.org/10.1109/access.2022.3187263 |
| primary_location.id | doi:10.1109/access.2022.3187263 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2485537415 |
| primary_location.source.issn | 2169-3536 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2169-3536 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IEEE Access |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/6514899/09815128.pdf |
| 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 | IEEE Access |
| primary_location.landing_page_url | https://doi.org/10.1109/access.2022.3187263 |
| publication_date | 2022-01-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3111980808, https://openalex.org/W2947385066, https://openalex.org/W6739901393, https://openalex.org/W3131825083, https://openalex.org/W6802996638, https://openalex.org/W6789909235, https://openalex.org/W3119438769, https://openalex.org/W3036763926, https://openalex.org/W3119074770, https://openalex.org/W2990216675, https://openalex.org/W3035068358, https://openalex.org/W3096570841, https://openalex.org/W2479423890, https://openalex.org/W3094472318, https://openalex.org/W3160638507, https://openalex.org/W6799271272, https://openalex.org/W3098267758, https://openalex.org/W3045725402, https://openalex.org/W2777602943, https://openalex.org/W2607855566, https://openalex.org/W2962737704, https://openalex.org/W2922277163, https://openalex.org/W2883170015, https://openalex.org/W3035017890, https://openalex.org/W2964072591, https://openalex.org/W2980248933, https://openalex.org/W2889811054, https://openalex.org/W3188542058, https://openalex.org/W3211783547, https://openalex.org/W3167118264, https://openalex.org/W4385245566, https://openalex.org/W3174770825 |
| referenced_works_count | 32 |
| abstract_inverted_index.a | 61, 75, 94, 124, 132 |
| abstract_inverted_index.As | 47 |
| abstract_inverted_index.In | 67, 120 |
| abstract_inverted_index.It | 99 |
| abstract_inverted_index.in | 13 |
| abstract_inverted_index.is | 3, 10, 50, 148 |
| abstract_inverted_index.of | 64, 78, 161, 173 |
| abstract_inverted_index.on | 150, 177 |
| abstract_inverted_index.or | 168 |
| abstract_inverted_index.to | 40, 69, 167 |
| abstract_inverted_index.we | 73, 122 |
| abstract_inverted_index.Our | 140 |
| abstract_inverted_index.and | 9, 22, 38, 59, 92, 106, 130, 144, 154 |
| abstract_inverted_index.are | 165 |
| abstract_inverted_index.but | 111 |
| abstract_inverted_index.few | 133 |
| abstract_inverted_index.for | 5, 81, 137 |
| abstract_inverted_index.has | 55 |
| abstract_inverted_index.not | 100 |
| abstract_inverted_index.the | 52, 70, 103, 108, 114, 159, 162, 174 |
| abstract_inverted_index.also | 112 |
| abstract_inverted_index.cost | 110 |
| abstract_inverted_index.even | 169 |
| abstract_inverted_index.fuse | 30 |
| abstract_inverted_index.many | 14 |
| abstract_inverted_index.only | 101 |
| abstract_inverted_index.some | 178 |
| abstract_inverted_index.than | 171 |
| abstract_inverted_index.time | 37 |
| abstract_inverted_index.used | 12 |
| abstract_inverted_index.with | 44 |
| abstract_inverted_index.above | 71 |
| abstract_inverted_index.close | 166 |
| abstract_inverted_index.every | 42, 48 |
| abstract_inverted_index.keeps | 87 |
| abstract_inverted_index.lacks | 60 |
| abstract_inverted_index.level | 63 |
| abstract_inverted_index.makes | 102 |
| abstract_inverted_index.model | 89, 104, 135 |
| abstract_inverted_index.novel | 76 |
| abstract_inverted_index.often | 29 |
| abstract_inverted_index.small | 95 |
| abstract_inverted_index.takes | 35 |
| abstract_inverted_index.those | 172 |
| abstract_inverted_index.train | 41, 131 |
| abstract_inverted_index.unity | 58 |
| abstract_inverted_index.which | 34, 86 |
| abstract_inverted_index.Genome | 156 |
| abstract_inverted_index.Prompt | 142 |
| abstract_inverted_index.Visual | 0, 155 |
| abstract_inverted_index.areas, | 15 |
| abstract_inverted_index.design | 123 |
| abstract_inverted_index.frozen | 91 |
| abstract_inverted_index.higher | 62, 170 |
| abstract_inverted_index.method | 77, 164 |
| abstract_inverted_index.module | 43, 49 |
| abstract_inverted_index.prompt | 128 |
| abstract_inverted_index.region | 32 |
| abstract_inverted_index.scenes | 8 |
| abstract_inverted_index.tasks, | 85 |
| abstract_inverted_index.tasks. | 139 |
| abstract_inverted_index.visual | 7, 17, 19, 82, 151 |
| abstract_inverted_index.widely | 11 |
| abstract_inverted_index.(PTAT), | 147 |
| abstract_inverted_index.crucial | 4 |
| abstract_inverted_index.logical | 65 |
| abstract_inverted_index.machine | 23 |
| abstract_inverted_index.method, | 141 |
| abstract_inverted_index.methods | 28, 176 |
| abstract_inverted_index.process | 54 |
| abstract_inverted_index.propose | 74 |
| abstract_inverted_index.reduces | 107 |
| abstract_inverted_index.results | 160 |
| abstract_inverted_index.trouble | 24 |
| abstract_inverted_index.unified | 105 |
| abstract_inverted_index.vector. | 98 |
| abstract_inverted_index.without | 117 |
| abstract_inverted_index.Finally, | 158 |
| abstract_inverted_index.Template | 143 |
| abstract_inverted_index.metrics. | 180 |
| abstract_inverted_index.modules, | 33 |
| abstract_inverted_index.multiple | 31 |
| abstract_inverted_index.proposed | 163 |
| abstract_inverted_index.question | 20 |
| abstract_inverted_index.response | 68 |
| abstract_inverted_index.samples. | 46 |
| abstract_inverted_index.sentence | 126 |
| abstract_inverted_index.template | 129 |
| abstract_inverted_index.training | 109 |
| abstract_inverted_index.achieving | 57 |
| abstract_inverted_index.addition, | 121 |
| abstract_inverted_index.datasets. | 157 |
| abstract_inverted_index.detection | 2, 27, 84, 153 |
| abstract_inverted_index.evaluated | 149 |
| abstract_inverted_index.extensive | 45 |
| abstract_inverted_index.including | 16 |
| abstract_inverted_index.maintains | 113 |
| abstract_inverted_index.optimizes | 93 |
| abstract_inverted_index.problems, | 72 |
| abstract_inverted_index.resources | 39 |
| abstract_inverted_index.training. | 119 |
| abstract_inverted_index.answering, | 21 |
| abstract_inverted_index.continuous | 96 |
| abstract_inverted_index.detection. | 25 |
| abstract_inverted_index.difficulty | 56 |
| abstract_inverted_index.downstream | 138 |
| abstract_inverted_index.evaluation | 179 |
| abstract_inverted_index.expression | 127 |
| abstract_inverted_index.multiscale | 118 |
| abstract_inverted_index.parameters | 90, 136 |
| abstract_inverted_index.Traditional | 26 |
| abstract_inverted_index.computation | 53 |
| abstract_inverted_index.navigation, | 18 |
| abstract_inverted_index.transformer | 88, 134 |
| abstract_inverted_index.Affix-Tuning | 145 |
| abstract_inverted_index.Transformers | 146 |
| abstract_inverted_index.affix-tuning | 79 |
| abstract_inverted_index.common-sense | 115 |
| abstract_inverted_index.considerable | 36 |
| abstract_inverted_index.independent, | 51 |
| abstract_inverted_index.relationship | 1, 83, 152 |
| abstract_inverted_index.transformers | 80 |
| abstract_inverted_index.reasonability | 116 |
| abstract_inverted_index.task-specific | 97 |
| abstract_inverted_index.understanding | 6 |
| abstract_inverted_index.reasonability. | 66 |
| abstract_inverted_index.state-of-the-art | 175 |
| abstract_inverted_index.vision-and-language | 125 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5101938303 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I4210129906, https://openalex.org/I4210131919 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6899999976158142 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.38960675 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |