CenterNet: Object Detection with Keypoint Triplets Article Swipe
Kaiwen Duan
,
Song Bai
,
Lingxi Xie
,
Honggang Qi
,
Qingming Huang
,
Qi Tian
·
YOU?
·
· 2019
· Open Access
·
YOU?
·
· 2019
· Open Access
·
In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions. This paper presents an efficient solution which explores the visual patterns within each cropped region with minimal costs. We build our framework upon a representative one-stage keypoint-based detector named CornerNet. Our approach, named CenterNet, detects each object as a triplet, rather than a pair, of keypoints, which improves both precision and recall. Accordingly, we design two customized modules named cascade corner pooling and center pooling, which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions, respectively. On the MS-COCO dataset, CenterNet achieves an AP of \textbf{47.0\%}, which outperforms all existing one-stage detectors by a large margin. Meanwhile, with a faster inference speed, CenterNet demonstrates quite comparable performance to the top-ranked two-stage detectors. Code is available at https://github.com/Duankaiwen/CenterNet.
Related Topics
Concepts
Pooling
Bounding overwatch
Computer science
Object (grammar)
Detector
Inference
Code (set theory)
Margin (machine learning)
Object detection
Cascade
Artificial intelligence
Pattern recognition (psychology)
Precision and recall
Machine learning
Engineering
Programming language
Set (abstract data type)
Chemical engineering
Telecommunications
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://arxiv.org/pdf/1904.08189
- OA Status
- green
- Cited By
- 65
- References
- 35
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2938756873
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W2938756873Canonical identifier for this work in OpenAlex
- Title
-
CenterNet: Object Detection with Keypoint TripletsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-04-17Full publication date if available
- Authors
-
Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, Qi TianList of authors in order
- Landing page
-
https://arxiv.org/pdf/1904.08189Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1904.08189Direct OA link when available
- Concepts
-
Pooling, Bounding overwatch, Computer science, Object (grammar), Detector, Inference, Code (set theory), Margin (machine learning), Object detection, Cascade, Artificial intelligence, Pattern recognition (psychology), Precision and recall, Machine learning, Engineering, Programming language, Set (abstract data type), Chemical engineering, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
65Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2023: 3, 2022: 2, 2021: 19Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.which | 68, 85, 116 |
| abstract_inverted_index.boxes, | 13 |
| abstract_inverted_index.center | 83 |
| abstract_inverted_index.costs. | 42 |
| abstract_inverted_index.design | 75 |
| abstract_inverted_index.number | 9 |
| abstract_inverted_index.object | 1, 11, 59 |
| abstract_inverted_index.rather | 62 |
| abstract_inverted_index.region | 39 |
| abstract_inverted_index.speed, | 129 |
| abstract_inverted_index.suffer | 6 |
| abstract_inverted_index.visual | 34 |
| abstract_inverted_index.within | 36 |
| abstract_inverted_index.cascade | 80 |
| abstract_inverted_index.central | 104 |
| abstract_inverted_index.corners | 97 |
| abstract_inverted_index.cropped | 24, 38 |
| abstract_inverted_index.detects | 57 |
| abstract_inverted_index.margin. | 124 |
| abstract_inverted_index.minimal | 41 |
| abstract_inverted_index.modules | 78 |
| abstract_inverted_index.We build | 43 |
| abstract_inverted_index.achieves | 111 |
| abstract_inverted_index.arguably | 14 |
| abstract_inverted_index.bounding | 12 |
| abstract_inverted_index.improves | 69 |
| abstract_inverted_index.patterns | 35 |
| abstract_inverted_index.pooling, | 84 |
| abstract_inverted_index.presents | 28 |
| abstract_inverted_index.regions, | 105 |
| abstract_inverted_index.regions. | 25 |
| abstract_inverted_index.solution | 31 |
| abstract_inverted_index.top-left | 94 |
| abstract_inverted_index.CenterNet | 110, 130 |
| abstract_inverted_index.approach, | 54 |
| abstract_inverted_index.available | 141 |
| abstract_inverted_index.detectors | 120 |
| abstract_inverted_index.efficient | 30 |
| abstract_inverted_index.enriching | 90 |
| abstract_inverted_index.framework | 45 |
| abstract_inverted_index.look into | 22 |
| abstract_inverted_index.one-stage | 49, 119 |
| abstract_inverted_index.precision | 71 |
| abstract_inverted_index.providing | 99 |
| abstract_inverted_index.two-stage | 137 |
| abstract_inverted_index.CenterNet, | 56 |
| abstract_inverted_index.CornerNet. | 52 |
| abstract_inverted_index.Meanwhile, | 125 |
| abstract_inverted_index.a triplet, | 61 |
| abstract_inverted_index.additional | 21 |
| abstract_inverted_index.approaches | 4 |
| abstract_inverted_index.comparable | 133 |
| abstract_inverted_index.customized | 77 |
| abstract_inverted_index.detection, | 2 |
| abstract_inverted_index.detectors. | 138 |
| abstract_inverted_index.keypoints, | 67 |
| abstract_inverted_index.and recall. | 72 |
| abstract_inverted_index.information | 101 |
| abstract_inverted_index.outperforms | 117 |
| abstract_inverted_index.performance | 134 |
| abstract_inverted_index.Accordingly, | 73 |
| abstract_inverted_index.all existing | 118 |
| abstract_inverted_index.bottom-right | 96 |
| abstract_inverted_index.demonstrates | 131 |
| abstract_inverted_index.of incorrect | 10 |
| abstract_inverted_index.respectively. | 106 |
| abstract_inverted_index.corner pooling | 81 |
| abstract_inverted_index.detector named | 51 |
| abstract_inverted_index.keypoint-based | 3, 50 |
| abstract_inverted_index.representative | 48 |
| abstract_inverted_index.the top-ranked | 136 |
| abstract_inverted_index.which explores | 32 |
| abstract_inverted_index.MS-COCO dataset, | 109 |
| abstract_inverted_index.\textbf{47.0\%}, | 115 |
| abstract_inverted_index.faster inference | 128 |
| abstract_inverted_index.more recognizable | 100 |
| abstract_inverted_index.information collected | 91 |
| abstract_inverted_index.at https://github.com/Duankaiwen/CenterNet. | 142 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.97123631 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |