Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding Article Swipe
Lingchen Meng
,
Xiyang Dai
,
Yinpeng Chen
,
Pengchuan Zhang
,
Dongdong Chen
,
Mengchen Liu
,
Jianfeng Wang
,
Zuxuan Wu
,
Lu Yuan
,
Yu–Gang Jiang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2206.03484
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2206.03484
Combining multiple datasets enables performance boost on many computer vision tasks. But similar trend has not been witnessed in object detection when combining multiple datasets due to two inconsistencies among detection datasets: taxonomy difference and domain gap. In this paper, we address these challenges by a new design (named Detection Hub) that is dataset-aware and category-aligned. It not only mitigates the dataset inconsistency but also provides coherent guidance for the detector to learn across multiple datasets. In particular, the dataset-aware design is achieved by learning a dataset embedding that is used to adapt object queries as well as convolutional kernels in detection heads. The categories across datasets are semantically aligned into a unified space by replacing one-hot category representations with word embedding and leveraging the semantic coherence of language embedding. Detection Hub fulfills the benefits of large data on object detection. Experiments demonstrate that joint training on multiple datasets achieves significant performance gains over training on each dataset alone. Detection Hub further achieves SoTA performance on UODB benchmark with wide variety of datasets.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2206.03484
- https://arxiv.org/pdf/2206.03484
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281733864
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281733864Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2206.03484Digital Object Identifier
- Title
-
Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language EmbeddingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-07Full publication date if available
- Authors
-
Lingchen Meng, Xiyang Dai, Yinpeng Chen, Pengchuan Zhang, Dongdong Chen, Mengchen Liu, Jianfeng Wang, Zuxuan Wu, Lu Yuan, Yu–Gang JiangList of authors in order
- Landing page
-
https://arxiv.org/abs/2206.03484Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2206.03484Direct 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/2206.03484Direct OA link when available
- Concepts
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Computer science, Embedding, Object detection, Benchmark (surveying), Word embedding, Artificial intelligence, Semantic gap, Variety (cybernetics), Machine learning, Data mining, Pattern recognition (psychology), Image (mathematics), Image retrieval, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.paper, | 39 |
| abstract_inverted_index.tasks. | 10 |
| abstract_inverted_index.vision | 9 |
| abstract_inverted_index.address | 41 |
| abstract_inverted_index.aligned | 109 |
| abstract_inverted_index.dataset | 61, 86, 157 |
| abstract_inverted_index.enables | 3 |
| abstract_inverted_index.further | 161 |
| abstract_inverted_index.kernels | 99 |
| abstract_inverted_index.one-hot | 116 |
| abstract_inverted_index.queries | 94 |
| abstract_inverted_index.similar | 12 |
| abstract_inverted_index.unified | 112 |
| abstract_inverted_index.variety | 170 |
| abstract_inverted_index.achieved | 82 |
| abstract_inverted_index.achieves | 149, 162 |
| abstract_inverted_index.benefits | 134 |
| abstract_inverted_index.category | 117 |
| abstract_inverted_index.coherent | 66 |
| abstract_inverted_index.computer | 8 |
| abstract_inverted_index.datasets | 2, 24, 106, 148 |
| abstract_inverted_index.detector | 70 |
| abstract_inverted_index.fulfills | 132 |
| abstract_inverted_index.guidance | 67 |
| abstract_inverted_index.language | 128 |
| abstract_inverted_index.learning | 84 |
| abstract_inverted_index.multiple | 1, 23, 74, 147 |
| abstract_inverted_index.provides | 65 |
| abstract_inverted_index.semantic | 125 |
| abstract_inverted_index.taxonomy | 32 |
| abstract_inverted_index.training | 145, 154 |
| abstract_inverted_index.Combining | 0 |
| abstract_inverted_index.Detection | 49, 130, 159 |
| abstract_inverted_index.benchmark | 167 |
| abstract_inverted_index.coherence | 126 |
| abstract_inverted_index.combining | 22 |
| abstract_inverted_index.datasets. | 75, 172 |
| abstract_inverted_index.datasets: | 31 |
| abstract_inverted_index.detection | 20, 30, 101 |
| abstract_inverted_index.embedding | 87, 121 |
| abstract_inverted_index.mitigates | 59 |
| abstract_inverted_index.replacing | 115 |
| abstract_inverted_index.witnessed | 17 |
| abstract_inverted_index.categories | 104 |
| abstract_inverted_index.challenges | 43 |
| abstract_inverted_index.detection. | 140 |
| abstract_inverted_index.difference | 33 |
| abstract_inverted_index.embedding. | 129 |
| abstract_inverted_index.leveraging | 123 |
| abstract_inverted_index.Experiments | 141 |
| abstract_inverted_index.demonstrate | 142 |
| abstract_inverted_index.particular, | 77 |
| abstract_inverted_index.performance | 4, 151, 164 |
| abstract_inverted_index.significant | 150 |
| abstract_inverted_index.semantically | 108 |
| abstract_inverted_index.convolutional | 98 |
| abstract_inverted_index.dataset-aware | 53, 79 |
| abstract_inverted_index.inconsistency | 62 |
| abstract_inverted_index.inconsistencies | 28 |
| abstract_inverted_index.representations | 118 |
| abstract_inverted_index.category-aligned. | 55 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.5899999737739563 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |