Corner Proposal Network for Anchor-free, Two-stage Object Detection Article Swipe
Kaiwen Duan
,
Lingxi Xie
,
Honggang Qi
,
Song Bai
,
Qingming Huang
,
Qi Tian
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.13816
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.13816
The goal of object detection is to determine the class and location of objects in an image. This paper proposes a novel anchor-free, two-stage framework which first extracts a number of object proposals by finding potential corner keypoint combinations and then assigns a class label to each proposal by a standalone classification stage. We demonstrate that these two stages are effective solutions for improving recall and precision, respectively, and they can be integrated into an end-to-end network. Our approach, dubbed Corner Proposal Network (CPN), enjoys the ability to detect objects of various scales and also avoids being confused by a large number of false-positive proposals. On the MS-COCO dataset, CPN achieves an AP of 49.2% which is competitive among state-of-the-art object detection methods. CPN also fits the scenario of computational efficiency, which achieves an AP of 41.6%/39.7% at 26.2/43.3 FPS, surpassing most competitors with the same inference speed. Code is available at https://github.com/Duankaiwen/CPNDet
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Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2007.13816
- https://arxiv.org/pdf/2007.13816
- OA Status
- green
- Cited By
- 25
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3045896538
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3045896538Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2007.13816Digital Object Identifier
- Title
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Corner Proposal Network for Anchor-free, Two-stage Object DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
-
2020-07-27Full publication date if available
- Authors
-
Kaiwen Duan, Lingxi Xie, Honggang Qi, Song Bai, Qingming Huang, Qi TianList of authors in order
- Landing page
-
https://arxiv.org/abs/2007.13816Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2007.13816Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2007.13816Direct OA link when available
- Concepts
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Computer science, Inference, Object (grammar), Class (philosophy), Object detection, Code (set theory), Image (mathematics), Artificial intelligence, Precision and recall, Competitor analysis, Pattern recognition (psychology), Data mining, Programming language, Set (abstract data type), Management, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
25Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 5, 2022: 2, 2021: 15, 2020: 2Per-year citation counts (last 5 years)
- References (count)
-
50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| 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/2007.13816 |
| primary_location.id | pmh:oai:arXiv.org:2007.13816 |
| 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/2007.13816 |
| 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/2007.13816 |
| publication_date | 2020-07-27 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W3034971973, https://openalex.org/W2102605133, https://openalex.org/W2307770531, https://openalex.org/W3106250896, https://openalex.org/W2963323244, https://openalex.org/W3107473354, https://openalex.org/W3018757597, https://openalex.org/W2962721361, https://openalex.org/W2963446712, https://openalex.org/W2982770724, https://openalex.org/W1861492603, https://openalex.org/W2963037989, https://openalex.org/W2964632911, https://openalex.org/W2604967609, https://openalex.org/W2129987527, https://openalex.org/W2916798096, https://openalex.org/W3098090606, https://openalex.org/W2963299996, https://openalex.org/W2950141105, https://openalex.org/W2963179609, https://openalex.org/W2613718673, https://openalex.org/W2935837427, https://openalex.org/W2565639579, https://openalex.org/W2964121718, https://openalex.org/W2163605009, https://openalex.org/W2407521645, https://openalex.org/W2988452521, https://openalex.org/W2955425717, https://openalex.org/W2963927307, https://openalex.org/W1536680647, https://openalex.org/W2601564443, https://openalex.org/W2031489346, https://openalex.org/W2555751471, https://openalex.org/W2986357608, https://openalex.org/W2919115771, https://openalex.org/W2899771611, https://openalex.org/W2964121744, https://openalex.org/W2936404177, https://openalex.org/W2989604896, https://openalex.org/W2194775991, https://openalex.org/W2963786238, https://openalex.org/W3012573144, https://openalex.org/W2970575838, https://openalex.org/W2964093967, https://openalex.org/W3035396860, https://openalex.org/W2963351448, https://openalex.org/W2963857746, https://openalex.org/W2963150697, https://openalex.org/W2964241181, https://openalex.org/W1686810756 |
| referenced_works_count | 50 |
| abstract_inverted_index.a | 20, 28, 42, 49, 99 |
| abstract_inverted_index.AP | 112, 134 |
| abstract_inverted_index.On | 105 |
| abstract_inverted_index.We | 53 |
| abstract_inverted_index.an | 15, 74, 111, 133 |
| abstract_inverted_index.at | 137, 151 |
| abstract_inverted_index.be | 71 |
| abstract_inverted_index.by | 33, 48, 98 |
| abstract_inverted_index.in | 14 |
| abstract_inverted_index.is | 5, 116, 149 |
| abstract_inverted_index.of | 2, 12, 30, 90, 102, 113, 128, 135 |
| abstract_inverted_index.to | 6, 45, 87 |
| abstract_inverted_index.CPN | 109, 123 |
| abstract_inverted_index.Our | 77 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 10, 39, 65, 68, 93 |
| abstract_inverted_index.are | 59 |
| abstract_inverted_index.can | 70 |
| abstract_inverted_index.for | 62 |
| abstract_inverted_index.the | 8, 85, 106, 126, 144 |
| abstract_inverted_index.two | 57 |
| abstract_inverted_index.Code | 148 |
| abstract_inverted_index.FPS, | 139 |
| abstract_inverted_index.This | 17 |
| abstract_inverted_index.also | 94, 124 |
| abstract_inverted_index.each | 46 |
| abstract_inverted_index.fits | 125 |
| abstract_inverted_index.goal | 1 |
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| abstract_inverted_index.same | 145 |
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| abstract_inverted_index.then | 40 |
| abstract_inverted_index.they | 69 |
| abstract_inverted_index.with | 143 |
| abstract_inverted_index.49.2% | 114 |
| abstract_inverted_index.among | 118 |
| abstract_inverted_index.being | 96 |
| abstract_inverted_index.class | 9, 43 |
| abstract_inverted_index.first | 26 |
| abstract_inverted_index.label | 44 |
| abstract_inverted_index.large | 100 |
| abstract_inverted_index.novel | 21 |
| abstract_inverted_index.paper | 18 |
| abstract_inverted_index.these | 56 |
| abstract_inverted_index.which | 25, 115, 131 |
| abstract_inverted_index.(CPN), | 83 |
| abstract_inverted_index.Corner | 80 |
| abstract_inverted_index.avoids | 95 |
| abstract_inverted_index.corner | 36 |
| abstract_inverted_index.detect | 88 |
| abstract_inverted_index.dubbed | 79 |
| abstract_inverted_index.enjoys | 84 |
| abstract_inverted_index.image. | 16 |
| abstract_inverted_index.number | 29, 101 |
| abstract_inverted_index.object | 3, 31, 120 |
| abstract_inverted_index.recall | 64 |
| abstract_inverted_index.scales | 92 |
| abstract_inverted_index.speed. | 147 |
| abstract_inverted_index.stage. | 52 |
| abstract_inverted_index.stages | 58 |
| abstract_inverted_index.MS-COCO | 107 |
| abstract_inverted_index.Network | 82 |
| abstract_inverted_index.ability | 86 |
| abstract_inverted_index.assigns | 41 |
| abstract_inverted_index.finding | 34 |
| abstract_inverted_index.objects | 13, 89 |
| abstract_inverted_index.various | 91 |
| abstract_inverted_index.Proposal | 81 |
| abstract_inverted_index.achieves | 110, 132 |
| abstract_inverted_index.confused | 97 |
| abstract_inverted_index.dataset, | 108 |
| abstract_inverted_index.extracts | 27 |
| abstract_inverted_index.keypoint | 37 |
| abstract_inverted_index.location | 11 |
| abstract_inverted_index.methods. | 122 |
| abstract_inverted_index.network. | 76 |
| abstract_inverted_index.proposal | 47 |
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| abstract_inverted_index.scenario | 127 |
| abstract_inverted_index.26.2/43.3 | 138 |
| abstract_inverted_index.approach, | 78 |
| abstract_inverted_index.available | 150 |
| abstract_inverted_index.detection | 4, 121 |
| abstract_inverted_index.determine | 7 |
| abstract_inverted_index.effective | 60 |
| abstract_inverted_index.framework | 24 |
| abstract_inverted_index.improving | 63 |
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| abstract_inverted_index.potential | 35 |
| abstract_inverted_index.proposals | 32 |
| abstract_inverted_index.solutions | 61 |
| abstract_inverted_index.two-stage | 23 |
| abstract_inverted_index.end-to-end | 75 |
| abstract_inverted_index.integrated | 72 |
| abstract_inverted_index.precision, | 66 |
| abstract_inverted_index.proposals. | 104 |
| abstract_inverted_index.standalone | 50 |
| abstract_inverted_index.surpassing | 140 |
| abstract_inverted_index.41.6%/39.7% | 136 |
| abstract_inverted_index.competitive | 117 |
| abstract_inverted_index.competitors | 142 |
| abstract_inverted_index.demonstrate | 54 |
| abstract_inverted_index.efficiency, | 130 |
| abstract_inverted_index.anchor-free, | 22 |
| abstract_inverted_index.combinations | 38 |
| abstract_inverted_index.computational | 129 |
| abstract_inverted_index.respectively, | 67 |
| abstract_inverted_index.classification | 51 |
| abstract_inverted_index.false-positive | 103 |
| abstract_inverted_index.state-of-the-art | 119 |
| abstract_inverted_index.https://github.com/Duankaiwen/CPNDet | 152 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |