Open-Set Automatic Target Recognition Article Swipe
Bardia Safaei
,
Vibashan VS
,
Celso M. de Melo
,
Shuowen Hu
,
Vishal M. Patel
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.05883
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.05883
Automatic Target Recognition (ATR) is a category of computer vision algorithms which attempts to recognize targets on data obtained from different sensors. ATR algorithms are extensively used in real-world scenarios such as military and surveillance applications. Existing ATR algorithms are developed for traditional closed-set methods where training and testing have the same class distribution. Thus, these algorithms have not been robust to unknown classes not seen during the training phase, limiting their utility in real-world applications. To this end, we propose an Open-set Automatic Target Recognition framework where we enable open-set recognition capability for ATR algorithms. In addition, we introduce a plugin Category-aware Binary Classifier (CBC) module to effectively tackle unknown classes seen during inference. The proposed CBC module can be easily integrated with any existing ATR algorithms and can be trained in an end-to-end manner. Experimental results show that the proposed approach outperforms many open-set methods on the DSIAC and CIFAR-10 datasets. To the best of our knowledge, this is the first work to address the open-set classification problem for ATR algorithms. Source code is available at: https://github.com/bardisafa/Open-set-ATR.
Related Topics
Concepts
Computer science
Automatic target recognition
Classifier (UML)
Artificial intelligence
Machine learning
Set (abstract data type)
Inference
Open set
Plug-in
Closed set
Training set
Open source
Class (philosophy)
Pattern recognition (psychology)
Data mining
Software
Programming language
Discrete mathematics
Synthetic aperture radar
Mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.05883
- https://arxiv.org/pdf/2211.05883
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309043983
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4309043983Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2211.05883Digital Object Identifier
- Title
-
Open-Set Automatic Target RecognitionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-10Full publication date if available
- Authors
-
Bardia Safaei, Vibashan VS, Celso M. de Melo, Shuowen Hu, Vishal M. PatelList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.05883Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2211.05883Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2211.05883Direct OA link when available
- Concepts
-
Computer science, Automatic target recognition, Classifier (UML), Artificial intelligence, Machine learning, Set (abstract data type), Inference, Open set, Plug-in, Closed set, Training set, Open source, Class (philosophy), Pattern recognition (psychology), Data mining, Software, Programming language, Discrete mathematics, Synthetic aperture radar, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4309043983 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2211.05883 |
| ids.doi | https://doi.org/10.48550/arxiv.2211.05883 |
| ids.openalex | https://openalex.org/W4309043983 |
| fwci | |
| type | preprint |
| title | Open-Set Automatic Target Recognition |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12389 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9785000085830688 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2202 |
| topics[0].subfield.display_name | Aerospace Engineering |
| topics[0].display_name | Infrared Target Detection Methodologies |
| topics[1].id | https://openalex.org/T11324 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9595999717712402 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1304 |
| topics[1].subfield.display_name | Biophysics |
| topics[1].display_name | Spectroscopy Techniques in Biomedical and Chemical Research |
| topics[2].id | https://openalex.org/T12676 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9510999917984009 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Machine Learning and ELM |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7598134279251099 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C117623542 |
| concepts[1].level | 3 |
| concepts[1].score | 0.7287601828575134 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q621974 |
| concepts[1].display_name | Automatic target recognition |
| concepts[2].id | https://openalex.org/C95623464 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6291386485099792 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[2].display_name | Classifier (UML) |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6201667785644531 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C119857082 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5660321116447449 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[4].display_name | Machine learning |
| concepts[5].id | https://openalex.org/C177264268 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5340906977653503 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[5].display_name | Set (abstract data type) |
| concepts[6].id | https://openalex.org/C2776214188 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5204089879989624 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[6].display_name | Inference |
| concepts[7].id | https://openalex.org/C42357961 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4687120318412781 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q213363 |
| concepts[7].display_name | Open set |
| concepts[8].id | https://openalex.org/C4924752 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4541186988353729 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q184148 |
| concepts[8].display_name | Plug-in |
| concepts[9].id | https://openalex.org/C164953516 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4443269371986389 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q320357 |
| concepts[9].display_name | Closed set |
| concepts[10].id | https://openalex.org/C51632099 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4275453984737396 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q3985153 |
| concepts[10].display_name | Training set |
| concepts[11].id | https://openalex.org/C3018397939 |
| concepts[11].level | 3 |
| concepts[11].score | 0.42301222681999207 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q3644502 |
| concepts[11].display_name | Open source |
| concepts[12].id | https://openalex.org/C2777212361 |
| concepts[12].level | 2 |
| concepts[12].score | 0.4118884801864624 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q5127848 |
| concepts[12].display_name | Class (philosophy) |
| concepts[13].id | https://openalex.org/C153180895 |
| concepts[13].level | 2 |
| concepts[13].score | 0.4082002639770508 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[13].display_name | Pattern recognition (psychology) |
| concepts[14].id | https://openalex.org/C124101348 |
| concepts[14].level | 1 |
| concepts[14].score | 0.38551202416419983 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[14].display_name | Data mining |
| concepts[15].id | https://openalex.org/C2777904410 |
| concepts[15].level | 2 |
| concepts[15].score | 0.17648741602897644 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7397 |
| concepts[15].display_name | Software |
| concepts[16].id | https://openalex.org/C199360897 |
| concepts[16].level | 1 |
| concepts[16].score | 0.11741036176681519 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[16].display_name | Programming language |
| concepts[17].id | https://openalex.org/C118615104 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q121416 |
| concepts[17].display_name | Discrete mathematics |
| concepts[18].id | https://openalex.org/C87360688 |
| concepts[18].level | 2 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q740686 |
| concepts[18].display_name | Synthetic aperture radar |
| concepts[19].id | https://openalex.org/C33923547 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[19].display_name | Mathematics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7598134279251099 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/automatic-target-recognition |
| keywords[1].score | 0.7287601828575134 |
| keywords[1].display_name | Automatic target recognition |
| keywords[2].id | https://openalex.org/keywords/classifier |
| keywords[2].score | 0.6291386485099792 |
| keywords[2].display_name | Classifier (UML) |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6201667785644531 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/machine-learning |
| keywords[4].score | 0.5660321116447449 |
| keywords[4].display_name | Machine learning |
| keywords[5].id | https://openalex.org/keywords/set |
| keywords[5].score | 0.5340906977653503 |
| keywords[5].display_name | Set (abstract data type) |
| keywords[6].id | https://openalex.org/keywords/inference |
| keywords[6].score | 0.5204089879989624 |
| keywords[6].display_name | Inference |
| keywords[7].id | https://openalex.org/keywords/open-set |
| keywords[7].score | 0.4687120318412781 |
| keywords[7].display_name | Open set |
| keywords[8].id | https://openalex.org/keywords/plug-in |
| keywords[8].score | 0.4541186988353729 |
| keywords[8].display_name | Plug-in |
| keywords[9].id | https://openalex.org/keywords/closed-set |
| keywords[9].score | 0.4443269371986389 |
| keywords[9].display_name | Closed set |
| keywords[10].id | https://openalex.org/keywords/training-set |
| keywords[10].score | 0.4275453984737396 |
| keywords[10].display_name | Training set |
| keywords[11].id | https://openalex.org/keywords/open-source |
| keywords[11].score | 0.42301222681999207 |
| keywords[11].display_name | Open source |
| keywords[12].id | https://openalex.org/keywords/class |
| keywords[12].score | 0.4118884801864624 |
| keywords[12].display_name | Class (philosophy) |
| keywords[13].id | https://openalex.org/keywords/pattern-recognition |
| keywords[13].score | 0.4082002639770508 |
| keywords[13].display_name | Pattern recognition (psychology) |
| keywords[14].id | https://openalex.org/keywords/data-mining |
| keywords[14].score | 0.38551202416419983 |
| keywords[14].display_name | Data mining |
| keywords[15].id | https://openalex.org/keywords/software |
| keywords[15].score | 0.17648741602897644 |
| keywords[15].display_name | Software |
| keywords[16].id | https://openalex.org/keywords/programming-language |
| keywords[16].score | 0.11741036176681519 |
| keywords[16].display_name | Programming language |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2211.05883 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2211.05883 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2211.05883 |
| locations[1].id | doi:10.48550/arxiv.2211.05883 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2211.05883 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5052225293 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9504-8637 |
| authorships[0].author.display_name | Bardia Safaei |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Safaei, Bardia |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5004330920 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Vibashan VS |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | VS, Vibashan |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5066072868 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2680-8334 |
| authorships[2].author.display_name | Celso M. de Melo |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | de Melo, Celso M. |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5002842383 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Shuowen Hu |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Hu, Shuowen |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5004716468 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-5239-692X |
| authorships[4].author.display_name | Vishal M. Patel |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Patel, Vishal M. |
| authorships[4].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2211.05883 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Open-Set Automatic Target Recognition |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12389 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9785000085830688 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2202 |
| primary_topic.subfield.display_name | Aerospace Engineering |
| primary_topic.display_name | Infrared Target Detection Methodologies |
| related_works | https://openalex.org/W2961173803, https://openalex.org/W4367394211, https://openalex.org/W4283819694, https://openalex.org/W2100941997, https://openalex.org/W2327391223, https://openalex.org/W4295141346, https://openalex.org/W2620754580, https://openalex.org/W1541330370, https://openalex.org/W2805008194, https://openalex.org/W2553531723 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2211.05883 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2211.05883 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2211.05883 |
| primary_location.id | pmh:oai:arXiv.org:2211.05883 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2211.05883 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2211.05883 |
| publication_date | 2022-11-10 |
| publication_year | 2022 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 5, 100 |
| abstract_inverted_index.In | 96 |
| abstract_inverted_index.To | 76, 153 |
| abstract_inverted_index.an | 81, 133 |
| abstract_inverted_index.as | 31 |
| abstract_inverted_index.be | 120, 130 |
| abstract_inverted_index.in | 27, 73, 132 |
| abstract_inverted_index.is | 4, 160, 175 |
| abstract_inverted_index.of | 7, 156 |
| abstract_inverted_index.on | 16, 147 |
| abstract_inverted_index.to | 13, 61, 107, 164 |
| abstract_inverted_index.we | 79, 88, 98 |
| abstract_inverted_index.ATR | 22, 37, 94, 126, 171 |
| abstract_inverted_index.CBC | 117 |
| abstract_inverted_index.The | 115 |
| abstract_inverted_index.and | 33, 47, 128, 150 |
| abstract_inverted_index.any | 124 |
| abstract_inverted_index.are | 24, 39 |
| abstract_inverted_index.at: | 177 |
| abstract_inverted_index.can | 119, 129 |
| abstract_inverted_index.for | 41, 93, 170 |
| abstract_inverted_index.not | 58, 64 |
| abstract_inverted_index.our | 157 |
| abstract_inverted_index.the | 50, 67, 140, 148, 154, 161, 166 |
| abstract_inverted_index.been | 59 |
| abstract_inverted_index.best | 155 |
| abstract_inverted_index.code | 174 |
| abstract_inverted_index.data | 17 |
| abstract_inverted_index.end, | 78 |
| abstract_inverted_index.from | 19 |
| abstract_inverted_index.have | 49, 57 |
| abstract_inverted_index.many | 144 |
| abstract_inverted_index.same | 51 |
| abstract_inverted_index.seen | 65, 112 |
| abstract_inverted_index.show | 138 |
| abstract_inverted_index.such | 30 |
| abstract_inverted_index.that | 139 |
| abstract_inverted_index.this | 77, 159 |
| abstract_inverted_index.used | 26 |
| abstract_inverted_index.with | 123 |
| abstract_inverted_index.work | 163 |
| abstract_inverted_index.(ATR) | 3 |
| abstract_inverted_index.(CBC) | 105 |
| abstract_inverted_index.DSIAC | 149 |
| abstract_inverted_index.Thus, | 54 |
| abstract_inverted_index.class | 52 |
| abstract_inverted_index.first | 162 |
| abstract_inverted_index.their | 71 |
| abstract_inverted_index.these | 55 |
| abstract_inverted_index.where | 45, 87 |
| abstract_inverted_index.which | 11 |
| abstract_inverted_index.Binary | 103 |
| abstract_inverted_index.Source | 173 |
| abstract_inverted_index.Target | 1, 84 |
| abstract_inverted_index.during | 66, 113 |
| abstract_inverted_index.easily | 121 |
| abstract_inverted_index.enable | 89 |
| abstract_inverted_index.module | 106, 118 |
| abstract_inverted_index.phase, | 69 |
| abstract_inverted_index.plugin | 101 |
| abstract_inverted_index.robust | 60 |
| abstract_inverted_index.tackle | 109 |
| abstract_inverted_index.vision | 9 |
| abstract_inverted_index.address | 165 |
| abstract_inverted_index.classes | 63, 111 |
| abstract_inverted_index.manner. | 135 |
| abstract_inverted_index.methods | 44, 146 |
| abstract_inverted_index.problem | 169 |
| abstract_inverted_index.propose | 80 |
| abstract_inverted_index.results | 137 |
| abstract_inverted_index.targets | 15 |
| abstract_inverted_index.testing | 48 |
| abstract_inverted_index.trained | 131 |
| abstract_inverted_index.unknown | 62, 110 |
| abstract_inverted_index.utility | 72 |
| abstract_inverted_index.CIFAR-10 | 151 |
| abstract_inverted_index.Existing | 36 |
| abstract_inverted_index.Open-set | 82 |
| abstract_inverted_index.approach | 142 |
| abstract_inverted_index.attempts | 12 |
| abstract_inverted_index.category | 6 |
| abstract_inverted_index.computer | 8 |
| abstract_inverted_index.existing | 125 |
| abstract_inverted_index.limiting | 70 |
| abstract_inverted_index.military | 32 |
| abstract_inverted_index.obtained | 18 |
| abstract_inverted_index.open-set | 90, 145, 167 |
| abstract_inverted_index.proposed | 116, 141 |
| abstract_inverted_index.sensors. | 21 |
| abstract_inverted_index.training | 46, 68 |
| abstract_inverted_index.Automatic | 0, 83 |
| abstract_inverted_index.addition, | 97 |
| abstract_inverted_index.available | 176 |
| abstract_inverted_index.datasets. | 152 |
| abstract_inverted_index.developed | 40 |
| abstract_inverted_index.different | 20 |
| abstract_inverted_index.framework | 86 |
| abstract_inverted_index.introduce | 99 |
| abstract_inverted_index.recognize | 14 |
| abstract_inverted_index.scenarios | 29 |
| abstract_inverted_index.Classifier | 104 |
| abstract_inverted_index.algorithms | 10, 23, 38, 56, 127 |
| abstract_inverted_index.capability | 92 |
| abstract_inverted_index.closed-set | 43 |
| abstract_inverted_index.end-to-end | 134 |
| abstract_inverted_index.inference. | 114 |
| abstract_inverted_index.integrated | 122 |
| abstract_inverted_index.knowledge, | 158 |
| abstract_inverted_index.real-world | 28, 74 |
| abstract_inverted_index.Recognition | 2, 85 |
| abstract_inverted_index.algorithms. | 95, 172 |
| abstract_inverted_index.effectively | 108 |
| abstract_inverted_index.extensively | 25 |
| abstract_inverted_index.outperforms | 143 |
| abstract_inverted_index.recognition | 91 |
| abstract_inverted_index.traditional | 42 |
| abstract_inverted_index.Experimental | 136 |
| abstract_inverted_index.surveillance | 34 |
| abstract_inverted_index.applications. | 35, 75 |
| abstract_inverted_index.distribution. | 53 |
| abstract_inverted_index.Category-aware | 102 |
| abstract_inverted_index.classification | 168 |
| abstract_inverted_index.https://github.com/bardisafa/Open-set-ATR. | 178 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |