Unifying Training and Inference for Panoptic Segmentation Article Swipe
Qizhu Li
,
Xiaojuan Qi
,
Philip H. S. Torr
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2001.04982
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2001.04982
We present an end-to-end network to bridge the gap between training and inference pipeline for panoptic segmentation, a task that seeks to partition an image into semantic regions for "stuff" and object instances for "things". In contrast to recent works, our network exploits a parametrised, yet lightweight panoptic segmentation submodule, powered by an end-to-end learnt dense instance affinity, to capture the probability that any pair of pixels belong to the same instance. This panoptic submodule gives rise to a novel propagation mechanism for panoptic logits and enables the network to output a coherent panoptic segmentation map for both "stuff" and "thing" classes, without any post-processing. Reaping the benefits of end-to-end training, our full system sets new records on the popular street scene dataset, Cityscapes, achieving 61.4 PQ with a ResNet-50 backbone using only the fine annotations. On the challenging COCO dataset, our ResNet-50-based network also delivers state-of-the-art accuracy of 43.4 PQ. Moreover, our network flexibly works with and without object mask cues, performing competitively under both settings, which is of interest for applications with computation budgets.
Related Topics
Concepts
Computer science
Segmentation
Inference
Artificial intelligence
Pipeline (software)
Panopticon
Object (grammar)
Pixel
Computer vision
Subnetwork
Task (project management)
Computation
Pattern recognition (psychology)
Algorithm
Computer network
Engineering
Law
Political science
Systems engineering
Politics
Programming language
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2001.04982
- https://arxiv.org/pdf/2001.04982
- OA Status
- green
- Cited By
- 6
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2999810194
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W2999810194Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2001.04982Digital Object Identifier
- Title
-
Unifying Training and Inference for Panoptic SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-01-14Full publication date if available
- Authors
-
Qizhu Li, Xiaojuan Qi, Philip H. S. TorrList of authors in order
- Landing page
-
https://arxiv.org/abs/2001.04982Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2001.04982Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2001.04982Direct OA link when available
- Concepts
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Computer science, Segmentation, Inference, Artificial intelligence, Pipeline (software), Panopticon, Object (grammar), Pixel, Computer vision, Subnetwork, Task (project management), Computation, Pattern recognition (psychology), Algorithm, Computer network, Engineering, Law, Political science, Systems engineering, Politics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2021: 3, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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20Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2001.04982 |
| primary_location.id | pmh:oai:arXiv.org:2001.04982 |
| 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/2001.04982 |
| 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/2001.04982 |
| publication_date | 2020-01-14 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2963091558, https://openalex.org/W2963236837, https://openalex.org/W2911831070, https://openalex.org/W2613718673, https://openalex.org/W2161236525, https://openalex.org/W2902499724, https://openalex.org/W2890782586, https://openalex.org/W2599765304, https://openalex.org/W2795587607, https://openalex.org/W2910628332, https://openalex.org/W2963350373, https://openalex.org/W2963420686, https://openalex.org/W2531409750, https://openalex.org/W2965182628, https://openalex.org/W2955058313, https://openalex.org/W2608858501, https://openalex.org/W2891601902, https://openalex.org/W2565639579, https://openalex.org/W3034355852, https://openalex.org/W2963342403 |
| referenced_works_count | 20 |
| abstract_inverted_index.a | 17, 43, 78, 91, 128 |
| abstract_inverted_index.In | 35 |
| abstract_inverted_index.On | 136 |
| abstract_inverted_index.PQ | 126 |
| abstract_inverted_index.We | 0 |
| abstract_inverted_index.an | 2, 23, 52 |
| abstract_inverted_index.by | 51 |
| abstract_inverted_index.is | 168 |
| abstract_inverted_index.of | 65, 108, 148, 169 |
| abstract_inverted_index.on | 117 |
| abstract_inverted_index.to | 5, 21, 37, 58, 68, 77, 89 |
| abstract_inverted_index.PQ. | 150 |
| abstract_inverted_index.and | 11, 30, 85, 99, 157 |
| abstract_inverted_index.any | 63, 103 |
| abstract_inverted_index.for | 14, 28, 33, 82, 96, 171 |
| abstract_inverted_index.gap | 8 |
| abstract_inverted_index.map | 95 |
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| abstract_inverted_index.our | 40, 111, 141, 152 |
| abstract_inverted_index.the | 7, 60, 69, 87, 106, 118, 133, 137 |
| abstract_inverted_index.yet | 45 |
| abstract_inverted_index.43.4 | 149 |
| abstract_inverted_index.61.4 | 125 |
| abstract_inverted_index.COCO | 139 |
| abstract_inverted_index.This | 72 |
| abstract_inverted_index.also | 144 |
| abstract_inverted_index.both | 97, 165 |
| abstract_inverted_index.fine | 134 |
| abstract_inverted_index.full | 112 |
| abstract_inverted_index.into | 25 |
| abstract_inverted_index.mask | 160 |
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| abstract_inverted_index.pair | 64 |
| abstract_inverted_index.rise | 76 |
| abstract_inverted_index.same | 70 |
| abstract_inverted_index.sets | 114 |
| abstract_inverted_index.task | 18 |
| abstract_inverted_index.that | 19, 62 |
| abstract_inverted_index.with | 127, 156, 173 |
| abstract_inverted_index.cues, | 161 |
| abstract_inverted_index.dense | 55 |
| abstract_inverted_index.gives | 75 |
| abstract_inverted_index.image | 24 |
| abstract_inverted_index.novel | 79 |
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| abstract_inverted_index.using | 131 |
| abstract_inverted_index.which | 167 |
| abstract_inverted_index.works | 155 |
| abstract_inverted_index.belong | 67 |
| abstract_inverted_index.bridge | 6 |
| abstract_inverted_index.learnt | 54 |
| abstract_inverted_index.logits | 84 |
| abstract_inverted_index.object | 31, 159 |
| abstract_inverted_index.output | 90 |
| abstract_inverted_index.pixels | 66 |
| abstract_inverted_index.recent | 38 |
| abstract_inverted_index.street | 120 |
| abstract_inverted_index.system | 113 |
| abstract_inverted_index.works, | 39 |
| abstract_inverted_index."stuff" | 29, 98 |
| abstract_inverted_index."thing" | 100 |
| abstract_inverted_index.Reaping | 105 |
| abstract_inverted_index.between | 9 |
| abstract_inverted_index.capture | 59 |
| abstract_inverted_index.enables | 86 |
| abstract_inverted_index.network | 4, 41, 88, 143, 153 |
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| abstract_inverted_index.powered | 50 |
| abstract_inverted_index.present | 1 |
| abstract_inverted_index.records | 116 |
| abstract_inverted_index.regions | 27 |
| abstract_inverted_index.without | 102, 158 |
| abstract_inverted_index.accuracy | 147 |
| abstract_inverted_index.backbone | 130 |
| abstract_inverted_index.benefits | 107 |
| abstract_inverted_index.budgets. | 175 |
| abstract_inverted_index.classes, | 101 |
| abstract_inverted_index.coherent | 92 |
| abstract_inverted_index.contrast | 36 |
| abstract_inverted_index.dataset, | 122, 140 |
| abstract_inverted_index.delivers | 145 |
| abstract_inverted_index.exploits | 42 |
| abstract_inverted_index.flexibly | 154 |
| abstract_inverted_index.instance | 56 |
| abstract_inverted_index.interest | 170 |
| abstract_inverted_index.panoptic | 15, 47, 73, 83, 93 |
| abstract_inverted_index.pipeline | 13 |
| abstract_inverted_index.semantic | 26 |
| abstract_inverted_index.training | 10 |
| abstract_inverted_index."things". | 34 |
| abstract_inverted_index.Moreover, | 151 |
| abstract_inverted_index.ResNet-50 | 129 |
| abstract_inverted_index.achieving | 124 |
| abstract_inverted_index.affinity, | 57 |
| abstract_inverted_index.inference | 12 |
| abstract_inverted_index.instance. | 71 |
| abstract_inverted_index.instances | 32 |
| abstract_inverted_index.mechanism | 81 |
| abstract_inverted_index.partition | 22 |
| abstract_inverted_index.settings, | 166 |
| abstract_inverted_index.submodule | 74 |
| abstract_inverted_index.training, | 110 |
| abstract_inverted_index.end-to-end | 3, 53, 109 |
| abstract_inverted_index.performing | 162 |
| abstract_inverted_index.submodule, | 49 |
| abstract_inverted_index.Cityscapes, | 123 |
| abstract_inverted_index.challenging | 138 |
| abstract_inverted_index.computation | 174 |
| abstract_inverted_index.lightweight | 46 |
| abstract_inverted_index.probability | 61 |
| abstract_inverted_index.propagation | 80 |
| abstract_inverted_index.annotations. | 135 |
| abstract_inverted_index.applications | 172 |
| abstract_inverted_index.segmentation | 48, 94 |
| abstract_inverted_index.competitively | 163 |
| abstract_inverted_index.parametrised, | 44 |
| abstract_inverted_index.segmentation, | 16 |
| abstract_inverted_index.ResNet-50-based | 142 |
| abstract_inverted_index.post-processing. | 104 |
| abstract_inverted_index.state-of-the-art | 146 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile |