Towards Online Domain Adaptive Object Detection Article Swipe
Vibashan VS
,
Poojan Oza
,
Vishal M. Patel
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.05289
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.05289
Existing object detection models assume both the training and test data are sampled from the same source domain. This assumption does not hold true when these detectors are deployed in real-world applications, where they encounter new visual domain. Unsupervised Domain Adaptation (UDA) methods are generally employed to mitigate the adverse effects caused by domain shift. Existing UDA methods operate in an offline manner where the model is first adapted towards the target domain and then deployed in real-world applications. However, this offline adaptation strategy is not suitable for real-world applications as the model frequently encounters new domain shifts. Hence, it becomes critical to develop a feasible UDA method that generalizes to these domain shifts encountered during deployment time in a continuous online manner. To this end, we propose a novel unified adaptation framework that adapts and improves generalization on the target domain in online settings. In particular, we introduce MemXformer - a cross-attention transformer-based memory module where items in the memory take advantage of domain shifts and record prototypical patterns of the target distribution. Further, MemXformer produces strong positive and negative pairs to guide a novel contrastive loss, which enhances target specific representation learning. Experiments on diverse detection benchmarks show that the proposed strategy can produce state-of-the-art performance in both online and offline settings. To the best of our knowledge, this is the first work to address online and offline adaptation settings for object detection. Code at https://github.com/Vibashan/memXformer-online-da
Related Topics
Concepts
Computer science
Domain (mathematical analysis)
Domain adaptation
Artificial intelligence
Object detection
Adaptation (eye)
Code (set theory)
Machine learning
Theoretical computer science
Pattern recognition (psychology)
Set (abstract data type)
Classifier (UML)
Programming language
Optics
Mathematical analysis
Physics
Mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2204.05289
- https://arxiv.org/pdf/2204.05289
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4223940149
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4223940149Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2204.05289Digital Object Identifier
- Title
-
Towards Online Domain Adaptive Object DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-11Full publication date if available
- Authors
-
Vibashan VS, Poojan Oza, Vishal M. PatelList of authors in order
- Landing page
-
https://arxiv.org/abs/2204.05289Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2204.05289Direct 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/2204.05289Direct OA link when available
- Concepts
-
Computer science, Domain (mathematical analysis), Domain adaptation, Artificial intelligence, Object detection, Adaptation (eye), Code (set theory), Machine learning, Theoretical computer science, Pattern recognition (psychology), Set (abstract data type), Classifier (UML), Programming language, Optics, Mathematical analysis, Physics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4223940149 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2204.05289 |
| ids.doi | https://doi.org/10.48550/arxiv.2204.05289 |
| ids.openalex | https://openalex.org/W4223940149 |
| fwci | |
| type | preprint |
| title | Towards Online Domain Adaptive Object Detection |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| 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 | 0.9993000030517578 |
| 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/T11775 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.98580002784729 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | COVID-19 diagnosis using AI |
| topics[2].id | https://openalex.org/T10036 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9851999878883362 |
| 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 | Advanced Neural Network Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8248911499977112 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C36503486 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5682669878005981 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11235244 |
| concepts[1].display_name | Domain (mathematical analysis) |
| concepts[2].id | https://openalex.org/C2776434776 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5082578063011169 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q19246213 |
| concepts[2].display_name | Domain adaptation |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4805926978588104 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C2776151529 |
| concepts[4].level | 3 |
| concepts[4].score | 0.44356605410575867 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3045304 |
| concepts[4].display_name | Object detection |
| concepts[5].id | https://openalex.org/C139807058 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4259253740310669 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q352374 |
| concepts[5].display_name | Adaptation (eye) |
| concepts[6].id | https://openalex.org/C2776760102 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4220028221607208 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5139990 |
| concepts[6].display_name | Code (set theory) |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4009774923324585 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C80444323 |
| concepts[8].level | 1 |
| concepts[8].score | 0.32070618867874146 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[8].display_name | Theoretical computer science |
| concepts[9].id | https://openalex.org/C153180895 |
| concepts[9].level | 2 |
| concepts[9].score | 0.19859561324119568 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[9].display_name | Pattern recognition (psychology) |
| concepts[10].id | https://openalex.org/C177264268 |
| concepts[10].level | 2 |
| concepts[10].score | 0.156334787607193 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[10].display_name | Set (abstract data type) |
| concepts[11].id | https://openalex.org/C95623464 |
| concepts[11].level | 2 |
| concepts[11].score | 0.12227484583854675 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[11].display_name | Classifier (UML) |
| concepts[12].id | https://openalex.org/C199360897 |
| concepts[12].level | 1 |
| concepts[12].score | 0.10997679829597473 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[12].display_name | Programming language |
| concepts[13].id | https://openalex.org/C120665830 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[13].display_name | Optics |
| concepts[14].id | https://openalex.org/C134306372 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[14].display_name | Mathematical analysis |
| 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 |
| concepts[16].id | https://openalex.org/C33923547 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[16].display_name | Mathematics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8248911499977112 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/domain |
| keywords[1].score | 0.5682669878005981 |
| keywords[1].display_name | Domain (mathematical analysis) |
| keywords[2].id | https://openalex.org/keywords/domain-adaptation |
| keywords[2].score | 0.5082578063011169 |
| keywords[2].display_name | Domain adaptation |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.4805926978588104 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/object-detection |
| keywords[4].score | 0.44356605410575867 |
| keywords[4].display_name | Object detection |
| keywords[5].id | https://openalex.org/keywords/adaptation |
| keywords[5].score | 0.4259253740310669 |
| keywords[5].display_name | Adaptation (eye) |
| keywords[6].id | https://openalex.org/keywords/code |
| keywords[6].score | 0.4220028221607208 |
| keywords[6].display_name | Code (set theory) |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.4009774923324585 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[8].score | 0.32070618867874146 |
| keywords[8].display_name | Theoretical computer science |
| keywords[9].id | https://openalex.org/keywords/pattern-recognition |
| keywords[9].score | 0.19859561324119568 |
| keywords[9].display_name | Pattern recognition (psychology) |
| keywords[10].id | https://openalex.org/keywords/set |
| keywords[10].score | 0.156334787607193 |
| keywords[10].display_name | Set (abstract data type) |
| keywords[11].id | https://openalex.org/keywords/classifier |
| keywords[11].score | 0.12227484583854675 |
| keywords[11].display_name | Classifier (UML) |
| keywords[12].id | https://openalex.org/keywords/programming-language |
| keywords[12].score | 0.10997679829597473 |
| keywords[12].display_name | Programming language |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2204.05289 |
| 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 | public-domain |
| locations[0].pdf_url | https://arxiv.org/pdf/2204.05289 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | https://openalex.org/licenses/public-domain |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2204.05289 |
| locations[1].id | doi:10.48550/arxiv.2204.05289 |
| 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 | public-domain |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/public-domain |
| 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.2204.05289 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5004330920 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Vibashan VS |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | VS, Vibashan |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5062459443 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5993-6650 |
| authorships[1].author.display_name | Poojan Oza |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Oza, Poojan |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5004716468 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5239-692X |
| authorships[2].author.display_name | Vishal M. Patel |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Patel, Vishal M. |
| authorships[2].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/2204.05289 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Towards Online Domain Adaptive Object Detection |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| 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 | 0.9993000030517578 |
| 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/W4394775207, https://openalex.org/W4389474468, https://openalex.org/W4300172004, https://openalex.org/W4321649381, https://openalex.org/W2997645659, https://openalex.org/W3180787869, https://openalex.org/W3203792196, https://openalex.org/W2955455867, https://openalex.org/W4295929828, https://openalex.org/W3156096827 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2204.05289 |
| 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 | public-domain |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2204.05289 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | https://openalex.org/licenses/public-domain |
| 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/2204.05289 |
| primary_location.id | pmh:oai:arXiv.org:2204.05289 |
| 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 | public-domain |
| primary_location.pdf_url | https://arxiv.org/pdf/2204.05289 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | https://openalex.org/licenses/public-domain |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2204.05289 |
| publication_date | 2022-04-11 |
| publication_year | 2022 |
| referenced_works_count | 0 |
| abstract_inverted_index.- | 150 |
| abstract_inverted_index.a | 104, 119, 128, 151, 184 |
| abstract_inverted_index.In | 145 |
| abstract_inverted_index.To | 123, 214 |
| abstract_inverted_index.an | 60 |
| abstract_inverted_index.as | 90 |
| abstract_inverted_index.at | 236 |
| abstract_inverted_index.by | 52 |
| abstract_inverted_index.in | 29, 59, 76, 118, 142, 158, 208 |
| abstract_inverted_index.is | 66, 84, 221 |
| abstract_inverted_index.it | 99 |
| abstract_inverted_index.of | 163, 170, 217 |
| abstract_inverted_index.on | 138, 195 |
| abstract_inverted_index.to | 46, 102, 110, 182, 225 |
| abstract_inverted_index.we | 126, 147 |
| abstract_inverted_index.UDA | 56, 106 |
| abstract_inverted_index.and | 8, 73, 135, 166, 179, 211, 228 |
| abstract_inverted_index.are | 11, 27, 43 |
| abstract_inverted_index.can | 204 |
| abstract_inverted_index.for | 87, 232 |
| abstract_inverted_index.new | 35, 95 |
| abstract_inverted_index.not | 21, 85 |
| abstract_inverted_index.our | 218 |
| abstract_inverted_index.the | 6, 14, 48, 64, 70, 91, 139, 159, 171, 201, 215, 222 |
| abstract_inverted_index.Code | 235 |
| abstract_inverted_index.This | 18 |
| abstract_inverted_index.best | 216 |
| abstract_inverted_index.both | 5, 209 |
| abstract_inverted_index.data | 10 |
| abstract_inverted_index.does | 20 |
| abstract_inverted_index.end, | 125 |
| abstract_inverted_index.from | 13 |
| abstract_inverted_index.hold | 22 |
| abstract_inverted_index.same | 15 |
| abstract_inverted_index.show | 199 |
| abstract_inverted_index.take | 161 |
| abstract_inverted_index.test | 9 |
| abstract_inverted_index.that | 108, 133, 200 |
| abstract_inverted_index.then | 74 |
| abstract_inverted_index.they | 33 |
| abstract_inverted_index.this | 80, 124, 220 |
| abstract_inverted_index.time | 117 |
| abstract_inverted_index.true | 23 |
| abstract_inverted_index.when | 24 |
| abstract_inverted_index.work | 224 |
| abstract_inverted_index.(UDA) | 41 |
| abstract_inverted_index.first | 67, 223 |
| abstract_inverted_index.guide | 183 |
| abstract_inverted_index.items | 157 |
| abstract_inverted_index.loss, | 187 |
| abstract_inverted_index.model | 65, 92 |
| abstract_inverted_index.novel | 129, 185 |
| abstract_inverted_index.pairs | 181 |
| abstract_inverted_index.these | 25, 111 |
| abstract_inverted_index.where | 32, 63, 156 |
| abstract_inverted_index.which | 188 |
| abstract_inverted_index.Domain | 39 |
| abstract_inverted_index.Hence, | 98 |
| abstract_inverted_index.adapts | 134 |
| abstract_inverted_index.assume | 4 |
| abstract_inverted_index.caused | 51 |
| abstract_inverted_index.domain | 53, 72, 96, 112, 141, 164 |
| abstract_inverted_index.during | 115 |
| abstract_inverted_index.manner | 62 |
| abstract_inverted_index.memory | 154, 160 |
| abstract_inverted_index.method | 107 |
| abstract_inverted_index.models | 3 |
| abstract_inverted_index.module | 155 |
| abstract_inverted_index.object | 1, 233 |
| abstract_inverted_index.online | 121, 143, 210, 227 |
| abstract_inverted_index.record | 167 |
| abstract_inverted_index.shift. | 54 |
| abstract_inverted_index.shifts | 113, 165 |
| abstract_inverted_index.source | 16 |
| abstract_inverted_index.strong | 177 |
| abstract_inverted_index.target | 71, 140, 172, 190 |
| abstract_inverted_index.visual | 36 |
| abstract_inverted_index.adapted | 68 |
| abstract_inverted_index.address | 226 |
| abstract_inverted_index.adverse | 49 |
| abstract_inverted_index.becomes | 100 |
| abstract_inverted_index.develop | 103 |
| abstract_inverted_index.diverse | 196 |
| abstract_inverted_index.domain. | 17, 37 |
| abstract_inverted_index.effects | 50 |
| abstract_inverted_index.manner. | 122 |
| abstract_inverted_index.methods | 42, 57 |
| abstract_inverted_index.offline | 61, 81, 212, 229 |
| abstract_inverted_index.operate | 58 |
| abstract_inverted_index.produce | 205 |
| abstract_inverted_index.propose | 127 |
| abstract_inverted_index.sampled | 12 |
| abstract_inverted_index.shifts. | 97 |
| abstract_inverted_index.towards | 69 |
| abstract_inverted_index.unified | 130 |
| abstract_inverted_index.Existing | 0, 55 |
| abstract_inverted_index.Further, | 174 |
| abstract_inverted_index.However, | 79 |
| abstract_inverted_index.critical | 101 |
| abstract_inverted_index.deployed | 28, 75 |
| abstract_inverted_index.employed | 45 |
| abstract_inverted_index.enhances | 189 |
| abstract_inverted_index.feasible | 105 |
| abstract_inverted_index.improves | 136 |
| abstract_inverted_index.mitigate | 47 |
| abstract_inverted_index.negative | 180 |
| abstract_inverted_index.patterns | 169 |
| abstract_inverted_index.positive | 178 |
| abstract_inverted_index.produces | 176 |
| abstract_inverted_index.proposed | 202 |
| abstract_inverted_index.settings | 231 |
| abstract_inverted_index.specific | 191 |
| abstract_inverted_index.strategy | 83, 203 |
| abstract_inverted_index.suitable | 86 |
| abstract_inverted_index.training | 7 |
| abstract_inverted_index.advantage | 162 |
| abstract_inverted_index.detection | 2, 197 |
| abstract_inverted_index.detectors | 26 |
| abstract_inverted_index.encounter | 34 |
| abstract_inverted_index.framework | 132 |
| abstract_inverted_index.generally | 44 |
| abstract_inverted_index.introduce | 148 |
| abstract_inverted_index.learning. | 193 |
| abstract_inverted_index.settings. | 144, 213 |
| abstract_inverted_index.Adaptation | 40 |
| abstract_inverted_index.MemXformer | 149, 175 |
| abstract_inverted_index.adaptation | 82, 131, 230 |
| abstract_inverted_index.assumption | 19 |
| abstract_inverted_index.benchmarks | 198 |
| abstract_inverted_index.continuous | 120 |
| abstract_inverted_index.deployment | 116 |
| abstract_inverted_index.detection. | 234 |
| abstract_inverted_index.encounters | 94 |
| abstract_inverted_index.frequently | 93 |
| abstract_inverted_index.knowledge, | 219 |
| abstract_inverted_index.real-world | 30, 77, 88 |
| abstract_inverted_index.Experiments | 194 |
| abstract_inverted_index.contrastive | 186 |
| abstract_inverted_index.encountered | 114 |
| abstract_inverted_index.generalizes | 109 |
| abstract_inverted_index.particular, | 146 |
| abstract_inverted_index.performance | 207 |
| abstract_inverted_index.Unsupervised | 38 |
| abstract_inverted_index.applications | 89 |
| abstract_inverted_index.prototypical | 168 |
| abstract_inverted_index.applications, | 31 |
| abstract_inverted_index.applications. | 78 |
| abstract_inverted_index.distribution. | 173 |
| abstract_inverted_index.generalization | 137 |
| abstract_inverted_index.representation | 192 |
| abstract_inverted_index.cross-attention | 152 |
| abstract_inverted_index.state-of-the-art | 206 |
| abstract_inverted_index.transformer-based | 153 |
| abstract_inverted_index.https://github.com/Vibashan/memXformer-online-da | 237 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |