HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation Article Swipe
Linglin Jing
,
Yiming Ding
,
Yunpeng Gao
,
Zhigang Wang
,
Xu Yan
,
Dong Wang
,
Gerald Schaefer
,
Hui Fang
,
Bin Zhao
,
Xuelong Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.16788
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2403.16788
Event-based semantic segmentation has gained popularity due to its capability to deal with scenarios under high-speed motion and extreme lighting conditions, which cannot be addressed by conventional RGB cameras. Since it is hard to annotate event data, previous approaches rely on event-to-image reconstruction to obtain pseudo labels for training. However, this will inevitably introduce noise, and learning from noisy pseudo labels, especially when generated from a single source, may reinforce the errors. This drawback is also called confirmation bias in pseudo-labeling. In this paper, we propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation, HPL-ESS, to alleviate the influence of noisy pseudo labels. In particular, we first employ a plain unsupervised domain adaptation framework as our baseline, which can generate a set of pseudo labels through self-training. Then, we incorporate offline event-to-image reconstruction into the framework, and obtain another set of pseudo labels by predicting segmentation maps on the reconstructed images. A noisy label learning strategy is designed to mix the two sets of pseudo labels and enhance the quality. Moreover, we propose a soft prototypical alignment module to further improve the consistency of target domain features. Extensive experiments show that our proposed method outperforms existing state-of-the-art methods by a large margin on the DSEC-Semantic dataset (+5.88% accuracy, +10.32% mIoU), which even surpasses several supervised methods.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2403.16788
- https://arxiv.org/pdf/2403.16788
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393213715
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4393213715Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2403.16788Digital Object Identifier
- Title
-
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-25Full publication date if available
- Authors
-
Linglin Jing, Yiming Ding, Yunpeng Gao, Zhigang Wang, Xu Yan, Dong Wang, Gerald Schaefer, Hui Fang, Bin Zhao, Xuelong LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2403.16788Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2403.16788Direct 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/2403.16788Direct OA link when available
- Concepts
-
Segmentation, Computer science, Event (particle physics), Artificial intelligence, Natural language processing, Pattern recognition (psychology), Physics, Quantum mechanicsTop 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/W4393213715 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2403.16788 |
| ids.doi | https://doi.org/10.48550/arxiv.2403.16788 |
| ids.openalex | https://openalex.org/W4393213715 |
| fwci | |
| type | preprint |
| title | HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10028 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9440000057220459 |
| 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 | Topic Modeling |
| topics[1].id | https://openalex.org/T10181 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9329000115394592 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Natural Language Processing Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C89600930 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6130781769752502 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[0].display_name | Segmentation |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5868017673492432 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2779662365 |
| concepts[2].level | 2 |
| concepts[2].score | 0.539127767086029 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q5416694 |
| concepts[2].display_name | Event (particle physics) |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5265117883682251 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C204321447 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4512280225753784 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[4].display_name | Natural language processing |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.32057738304138184 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C121332964 |
| concepts[6].level | 0 |
| concepts[6].score | 0.07465866208076477 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[6].display_name | Physics |
| concepts[7].id | https://openalex.org/C62520636 |
| concepts[7].level | 1 |
| concepts[7].score | 0.0 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[7].display_name | Quantum mechanics |
| keywords[0].id | https://openalex.org/keywords/segmentation |
| keywords[0].score | 0.6130781769752502 |
| keywords[0].display_name | Segmentation |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5868017673492432 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/event |
| keywords[2].score | 0.539127767086029 |
| keywords[2].display_name | Event (particle physics) |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5265117883682251 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/natural-language-processing |
| keywords[4].score | 0.4512280225753784 |
| keywords[4].display_name | Natural language processing |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.32057738304138184 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/physics |
| keywords[6].score | 0.07465866208076477 |
| keywords[6].display_name | Physics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2403.16788 |
| 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 | cc-by |
| locations[0].pdf_url | https://arxiv.org/pdf/2403.16788 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2403.16788 |
| locations[1].id | doi:10.48550/arxiv.2403.16788 |
| 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 | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| 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.2403.16788 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5044802124 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1287-4066 |
| authorships[0].author.display_name | Linglin Jing |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jing, Linglin |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5101524238 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0990-735X |
| authorships[1].author.display_name | Yiming Ding |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ding, Yiming |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5076911490 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9932-5822 |
| authorships[2].author.display_name | Yunpeng Gao |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Gao, Yunpeng |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5101606199 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-0472-9961 |
| authorships[3].author.display_name | Zhigang Wang |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Wang, Zhigang |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5062932135 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-5926-1182 |
| authorships[4].author.display_name | Xu Yan |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yan, Xu |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5100391517 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-9599-8023 |
| authorships[5].author.display_name | Dong Wang |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Wang, Dong |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5105462955 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-1292-7674 |
| authorships[6].author.display_name | Gerald Schaefer |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Schaefer, Gerald |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5101716412 |
| authorships[7].author.orcid | https://orcid.org/0009-0003-1904-787X |
| authorships[7].author.display_name | Hui Fang |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Fang, Hui |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5102258300 |
| authorships[8].author.orcid | |
| authorships[8].author.display_name | Bin Zhao |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Zhao, Bin |
| authorships[8].is_corresponding | False |
| authorships[9].author.id | https://openalex.org/A5106943753 |
| authorships[9].author.orcid | https://orcid.org/0000-0003-2924-946X |
| authorships[9].author.display_name | Xuelong Li |
| authorships[9].author_position | last |
| authorships[9].raw_author_name | Li, Xuelong |
| authorships[9].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/2403.16788 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10028 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9440000057220459 |
| 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 | Topic Modeling |
| related_works | https://openalex.org/W4379231730, https://openalex.org/W4389858081, https://openalex.org/W4324315429, https://openalex.org/W2501551404, https://openalex.org/W4385583601, https://openalex.org/W2113201962, https://openalex.org/W4298131179, https://openalex.org/W2033914206, https://openalex.org/W2042327336, https://openalex.org/W3204019825 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2403.16788 |
| 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 | cc-by |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2403.16788 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| 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/2403.16788 |
| primary_location.id | pmh:oai:arXiv.org:2403.16788 |
| 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 | cc-by |
| primary_location.pdf_url | https://arxiv.org/pdf/2403.16788 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2403.16788 |
| publication_date | 2024-03-25 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 153 |
| abstract_inverted_index.a | 65, 86, 110, 122, 175, 201 |
| abstract_inverted_index.In | 81, 105 |
| abstract_inverted_index.as | 116 |
| abstract_inverted_index.be | 23 |
| abstract_inverted_index.by | 25, 145, 200 |
| abstract_inverted_index.in | 79 |
| abstract_inverted_index.is | 31, 74, 158 |
| abstract_inverted_index.it | 30 |
| abstract_inverted_index.of | 101, 124, 142, 165, 185 |
| abstract_inverted_index.on | 40, 149, 204 |
| abstract_inverted_index.to | 7, 10, 33, 43, 97, 160, 180 |
| abstract_inverted_index.we | 84, 107, 130, 173 |
| abstract_inverted_index.RGB | 27 |
| abstract_inverted_index.and | 17, 55, 138, 168 |
| abstract_inverted_index.can | 120 |
| abstract_inverted_index.due | 6 |
| abstract_inverted_index.for | 47, 91 |
| abstract_inverted_index.has | 3 |
| abstract_inverted_index.its | 8 |
| abstract_inverted_index.may | 68 |
| abstract_inverted_index.mix | 161 |
| abstract_inverted_index.our | 117, 193 |
| abstract_inverted_index.set | 123, 141 |
| abstract_inverted_index.the | 70, 99, 136, 150, 162, 170, 183, 205 |
| abstract_inverted_index.two | 163 |
| abstract_inverted_index.This | 72 |
| abstract_inverted_index.also | 75 |
| abstract_inverted_index.bias | 78 |
| abstract_inverted_index.deal | 11 |
| abstract_inverted_index.even | 213 |
| abstract_inverted_index.from | 57, 64 |
| abstract_inverted_index.hard | 32 |
| abstract_inverted_index.into | 135 |
| abstract_inverted_index.maps | 148 |
| abstract_inverted_index.rely | 39 |
| abstract_inverted_index.sets | 164 |
| abstract_inverted_index.show | 191 |
| abstract_inverted_index.soft | 176 |
| abstract_inverted_index.that | 192 |
| abstract_inverted_index.this | 50, 82 |
| abstract_inverted_index.when | 62 |
| abstract_inverted_index.will | 51 |
| abstract_inverted_index.with | 12 |
| abstract_inverted_index.Since | 29 |
| abstract_inverted_index.Then, | 129 |
| abstract_inverted_index.data, | 36 |
| abstract_inverted_index.event | 35 |
| abstract_inverted_index.first | 108 |
| abstract_inverted_index.label | 155 |
| abstract_inverted_index.large | 202 |
| abstract_inverted_index.noisy | 58, 102, 154 |
| abstract_inverted_index.novel | 87 |
| abstract_inverted_index.plain | 111 |
| abstract_inverted_index.under | 14 |
| abstract_inverted_index.which | 21, 119, 212 |
| abstract_inverted_index.called | 76 |
| abstract_inverted_index.cannot | 22 |
| abstract_inverted_index.domain | 113, 187 |
| abstract_inverted_index.employ | 109 |
| abstract_inverted_index.gained | 4 |
| abstract_inverted_index.hybrid | 88 |
| abstract_inverted_index.labels | 46, 126, 144, 167 |
| abstract_inverted_index.mIoU), | 211 |
| abstract_inverted_index.margin | 203 |
| abstract_inverted_index.method | 195 |
| abstract_inverted_index.module | 179 |
| abstract_inverted_index.motion | 16 |
| abstract_inverted_index.noise, | 54 |
| abstract_inverted_index.obtain | 44, 139 |
| abstract_inverted_index.paper, | 83 |
| abstract_inverted_index.pseudo | 45, 59, 103, 125, 143, 166 |
| abstract_inverted_index.single | 66 |
| abstract_inverted_index.target | 186 |
| abstract_inverted_index.(+5.88% | 208 |
| abstract_inverted_index.+10.32% | 210 |
| abstract_inverted_index.another | 140 |
| abstract_inverted_index.dataset | 207 |
| abstract_inverted_index.enhance | 169 |
| abstract_inverted_index.errors. | 71 |
| abstract_inverted_index.extreme | 18 |
| abstract_inverted_index.further | 181 |
| abstract_inverted_index.images. | 152 |
| abstract_inverted_index.improve | 182 |
| abstract_inverted_index.labels, | 60 |
| abstract_inverted_index.labels. | 104 |
| abstract_inverted_index.methods | 199 |
| abstract_inverted_index.offline | 132 |
| abstract_inverted_index.propose | 85, 174 |
| abstract_inverted_index.several | 215 |
| abstract_inverted_index.source, | 67 |
| abstract_inverted_index.through | 127 |
| abstract_inverted_index.HPL-ESS, | 96 |
| abstract_inverted_index.However, | 49 |
| abstract_inverted_index.annotate | 34 |
| abstract_inverted_index.cameras. | 28 |
| abstract_inverted_index.designed | 159 |
| abstract_inverted_index.drawback | 73 |
| abstract_inverted_index.existing | 197 |
| abstract_inverted_index.generate | 121 |
| abstract_inverted_index.learning | 56, 156 |
| abstract_inverted_index.lighting | 19 |
| abstract_inverted_index.methods. | 217 |
| abstract_inverted_index.previous | 37 |
| abstract_inverted_index.proposed | 194 |
| abstract_inverted_index.quality. | 171 |
| abstract_inverted_index.semantic | 1, 94 |
| abstract_inverted_index.strategy | 157 |
| abstract_inverted_index.Extensive | 189 |
| abstract_inverted_index.Moreover, | 172 |
| abstract_inverted_index.accuracy, | 209 |
| abstract_inverted_index.addressed | 24 |
| abstract_inverted_index.alignment | 178 |
| abstract_inverted_index.alleviate | 98 |
| abstract_inverted_index.baseline, | 118 |
| abstract_inverted_index.features. | 188 |
| abstract_inverted_index.framework | 90, 115 |
| abstract_inverted_index.generated | 63 |
| abstract_inverted_index.influence | 100 |
| abstract_inverted_index.introduce | 53 |
| abstract_inverted_index.reinforce | 69 |
| abstract_inverted_index.scenarios | 13 |
| abstract_inverted_index.surpasses | 214 |
| abstract_inverted_index.training. | 48 |
| abstract_inverted_index.adaptation | 114 |
| abstract_inverted_index.approaches | 38 |
| abstract_inverted_index.capability | 9 |
| abstract_inverted_index.especially | 61 |
| abstract_inverted_index.framework, | 137 |
| abstract_inverted_index.high-speed | 15 |
| abstract_inverted_index.inevitably | 52 |
| abstract_inverted_index.popularity | 5 |
| abstract_inverted_index.predicting | 146 |
| abstract_inverted_index.supervised | 216 |
| abstract_inverted_index.Event-based | 0 |
| abstract_inverted_index.conditions, | 20 |
| abstract_inverted_index.consistency | 184 |
| abstract_inverted_index.event-based | 93 |
| abstract_inverted_index.experiments | 190 |
| abstract_inverted_index.incorporate | 131 |
| abstract_inverted_index.outperforms | 196 |
| abstract_inverted_index.particular, | 106 |
| abstract_inverted_index.confirmation | 77 |
| abstract_inverted_index.conventional | 26 |
| abstract_inverted_index.prototypical | 177 |
| abstract_inverted_index.segmentation | 2, 147 |
| abstract_inverted_index.unsupervised | 92, 112 |
| abstract_inverted_index.DSEC-Semantic | 206 |
| abstract_inverted_index.reconstructed | 151 |
| abstract_inverted_index.segmentation, | 95 |
| abstract_inverted_index.event-to-image | 41, 133 |
| abstract_inverted_index.reconstruction | 42, 134 |
| abstract_inverted_index.self-training. | 128 |
| abstract_inverted_index.pseudo-labeling | 89 |
| abstract_inverted_index.pseudo-labeling. | 80 |
| abstract_inverted_index.state-of-the-art | 198 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |