Learning to Segment Every Thing Article Swipe
Ronghang Hu
,
Piotr Dollár
,
Kaiming He
,
Trevor Darrell
,
Ross Girshick
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1711.10370
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1711.10370
Most methods for object instance segmentation require all training examples to be labeled with segmentation masks. This requirement makes it expensive to annotate new categories and has restricted instance segmentation models to ~100 well-annotated classes. The goal of this paper is to propose a new partially supervised training paradigm, together with a novel weight transfer function, that enables training instance segmentation models on a large set of categories all of which have box annotations, but only a small fraction of which have mask annotations. These contributions allow us to train Mask R-CNN to detect and segment 3000 visual concepts using box annotations from the Visual Genome dataset and mask annotations from the 80 classes in the COCO dataset. We evaluate our approach in a controlled study on the COCO dataset. This work is a first step towards instance segmentation models that have broad comprehension of the visual world.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1711.10370
- https://arxiv.org/pdf/1711.10370
- OA Status
- green
- Cited By
- 15
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2768923469
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2768923469Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1711.10370Digital Object Identifier
- Title
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Learning to Segment Every ThingWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
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2017-11-28Full publication date if available
- Authors
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Ronghang Hu, Piotr Dollár, Kaiming He, Trevor Darrell, Ross GirshickList of authors in order
- Landing page
-
https://arxiv.org/abs/1711.10370Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1711.10370Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1711.10370Direct OA link when available
- Concepts
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Segmentation, Computer science, Artificial intelligence, Object (grammar), Set (abstract data type), Annotation, Pattern recognition (psychology), Function (biology), Training set, Machine learning, Natural language processing, Evolutionary biology, Biology, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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15Total citation count in OpenAlex
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2021: 2, 2020: 2, 2019: 8, 2018: 3Per-year citation counts (last 5 years)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.set | 65 |
| abstract_inverted_index.the | 103, 111, 115, 127, 145 |
| abstract_inverted_index.3000 | 96 |
| abstract_inverted_index.COCO | 116, 128 |
| abstract_inverted_index.Mask | 90 |
| abstract_inverted_index.Most | 0 |
| abstract_inverted_index.This | 16, 130 |
| abstract_inverted_index.from | 102, 110 |
| abstract_inverted_index.goal | 36 |
| abstract_inverted_index.have | 71, 81, 141 |
| abstract_inverted_index.mask | 82, 108 |
| abstract_inverted_index.only | 75 |
| abstract_inverted_index.step | 135 |
| abstract_inverted_index.that | 56, 140 |
| abstract_inverted_index.this | 38 |
| abstract_inverted_index.with | 13, 50 |
| abstract_inverted_index.work | 131 |
| abstract_inverted_index.~100 | 32 |
| abstract_inverted_index.R-CNN | 91 |
| abstract_inverted_index.These | 84 |
| abstract_inverted_index.allow | 86 |
| abstract_inverted_index.broad | 142 |
| abstract_inverted_index.first | 134 |
| abstract_inverted_index.large | 64 |
| abstract_inverted_index.makes | 18 |
| abstract_inverted_index.novel | 52 |
| abstract_inverted_index.paper | 39 |
| abstract_inverted_index.small | 77 |
| abstract_inverted_index.study | 125 |
| abstract_inverted_index.train | 89 |
| abstract_inverted_index.using | 99 |
| abstract_inverted_index.which | 70, 80 |
| abstract_inverted_index.Genome | 105 |
| abstract_inverted_index.Visual | 104 |
| abstract_inverted_index.detect | 93 |
| abstract_inverted_index.masks. | 15 |
| abstract_inverted_index.models | 30, 61, 139 |
| abstract_inverted_index.object | 3 |
| abstract_inverted_index.visual | 97, 146 |
| abstract_inverted_index.weight | 53 |
| abstract_inverted_index.world. | 147 |
| abstract_inverted_index.classes | 113 |
| abstract_inverted_index.dataset | 106 |
| abstract_inverted_index.enables | 57 |
| abstract_inverted_index.labeled | 12 |
| abstract_inverted_index.methods | 1 |
| abstract_inverted_index.propose | 42 |
| abstract_inverted_index.require | 6 |
| abstract_inverted_index.segment | 95 |
| abstract_inverted_index.towards | 136 |
| abstract_inverted_index.annotate | 22 |
| abstract_inverted_index.approach | 121 |
| abstract_inverted_index.classes. | 34 |
| abstract_inverted_index.concepts | 98 |
| abstract_inverted_index.dataset. | 117, 129 |
| abstract_inverted_index.evaluate | 119 |
| abstract_inverted_index.examples | 9 |
| abstract_inverted_index.fraction | 78 |
| abstract_inverted_index.instance | 4, 28, 59, 137 |
| abstract_inverted_index.together | 49 |
| abstract_inverted_index.training | 8, 47, 58 |
| abstract_inverted_index.transfer | 54 |
| abstract_inverted_index.expensive | 20 |
| abstract_inverted_index.function, | 55 |
| abstract_inverted_index.paradigm, | 48 |
| abstract_inverted_index.partially | 45 |
| abstract_inverted_index.categories | 24, 67 |
| abstract_inverted_index.controlled | 124 |
| abstract_inverted_index.restricted | 27 |
| abstract_inverted_index.supervised | 46 |
| abstract_inverted_index.annotations | 101, 109 |
| abstract_inverted_index.requirement | 17 |
| abstract_inverted_index.annotations, | 73 |
| abstract_inverted_index.annotations. | 83 |
| abstract_inverted_index.segmentation | 5, 14, 29, 60, 138 |
| abstract_inverted_index.comprehension | 143 |
| abstract_inverted_index.contributions | 85 |
| abstract_inverted_index.well-annotated | 33 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |