ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models Article Swipe
Chunyuan Li
,
Haotian Liu
,
Liunian Harold Li
,
Pengchuan Zhang
,
Jyoti Aneja
,
Jianwei Yang
,
Ping Jin
,
Yong Jae Lee
,
Houdong Hu
,
Zicheng Liu
,
Jianfeng Gao
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.08790
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.08790
Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks. However, it remains challenging to evaluate the transferablity of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. To tackle this, we build ELEVATER (Evaluation of Language-augmented Visual Task-level Transfer), the first benchmark and toolkit for evaluating(pre-trained) language-augmented visual models. ELEVATER is composed of three components. (i) Datasets. As downstream evaluation suites, it consists of 20 image classification datasets and 35 object detection datasets, each of which is augmented with external knowledge. (ii) Toolkit. An automatic hyper-parameter tuning toolkit is developed to facilitate model evaluation on downstream tasks. (iii) Metrics. A variety of evaluation metrics are used to measure sample-efficiency (zero-shot and few-shot) and parameter-efficiency (linear probing and full model fine-tuning). ELEVATER is a platform for Computer Vision in the Wild (CVinW), and is publicly released at at https://computer-vision-in-the-wild.github.io/ELEVATER/
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2204.08790
- https://arxiv.org/pdf/2204.08790
- OA Status
- green
- Cited By
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224246420
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https://openalex.org/W4224246420Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2204.08790Digital Object Identifier
- Title
-
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-19Full publication date if available
- Authors
-
Chunyuan Li, Haotian Liu, Liunian Harold Li, Pengchuan Zhang, Jyoti Aneja, Jianwei Yang, Ping Jin, Yong Jae Lee, Houdong Hu, Zicheng Liu, Jianfeng GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2204.08790Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2204.08790Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2204.08790Direct OA link when available
- Concepts
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Computer science, Benchmark (surveying), Variety (cybernetics), Artificial intelligence, Task (project management), Machine learning, Transferability, Transfer of learning, Measure (data warehouse), Object (grammar), Natural language processing, Data mining, Logit, Economics, Geography, Management, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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64Total citation count in OpenAlex
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2025: 7, 2024: 34, 2023: 22, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.lack | 48 |
| abstract_inverted_index.used | 134 |
| abstract_inverted_index.with | 107 |
| abstract_inverted_index.(iii) | 126 |
| abstract_inverted_index.build | 60 |
| abstract_inverted_index.first | 69 |
| abstract_inverted_index.great | 10 |
| abstract_inverted_index.image | 94 |
| abstract_inverted_index.model | 121, 147 |
| abstract_inverted_index.shown | 9 |
| abstract_inverted_index.these | 20, 43 |
| abstract_inverted_index.this, | 58 |
| abstract_inverted_index.three | 82 |
| abstract_inverted_index.which | 104 |
| abstract_inverted_index.Vision | 155 |
| abstract_inverted_index.Visual | 65 |
| abstract_inverted_index.models | 23, 44 |
| abstract_inverted_index.number | 14 |
| abstract_inverted_index.object | 99 |
| abstract_inverted_index.public | 54 |
| abstract_inverted_index.strong | 25 |
| abstract_inverted_index.tackle | 57 |
| abstract_inverted_index.tasks. | 33, 125 |
| abstract_inverted_index.tuning | 115 |
| abstract_inverted_index.visual | 1, 22, 76 |
| abstract_inverted_index.works. | 17 |
| abstract_inverted_index.(linear | 143 |
| abstract_inverted_index.measure | 136 |
| abstract_inverted_index.metrics | 132 |
| abstract_inverted_index.models. | 77 |
| abstract_inverted_index.natural | 4 |
| abstract_inverted_index.probing | 144 |
| abstract_inverted_index.promise | 11 |
| abstract_inverted_index.remains | 36 |
| abstract_inverted_index.suites, | 89 |
| abstract_inverted_index.toolkit | 72, 116 |
| abstract_inverted_index.variety | 29, 129 |
| abstract_inverted_index.(CVinW), | 159 |
| abstract_inverted_index.Computer | 154 |
| abstract_inverted_index.ELEVATER | 61, 78, 149 |
| abstract_inverted_index.However, | 34 |
| abstract_inverted_index.Learning | 0 |
| abstract_inverted_index.Metrics. | 127 |
| abstract_inverted_index.Toolkit. | 111 |
| abstract_inverted_index.composed | 80 |
| abstract_inverted_index.consists | 91 |
| abstract_inverted_index.datasets | 31, 96 |
| abstract_inverted_index.evaluate | 39 |
| abstract_inverted_index.external | 108 |
| abstract_inverted_index.general, | 19 |
| abstract_inverted_index.language | 5 |
| abstract_inverted_index.platform | 152 |
| abstract_inverted_index.publicly | 162 |
| abstract_inverted_index.recently | 8 |
| abstract_inverted_index.released | 163 |
| abstract_inverted_index.toolkits | 52 |
| abstract_inverted_index.Datasets. | 85 |
| abstract_inverted_index.augmented | 106 |
| abstract_inverted_index.automatic | 113 |
| abstract_inverted_index.benchmark | 70 |
| abstract_inverted_index.datasets, | 101 |
| abstract_inverted_index.detection | 100 |
| abstract_inverted_index.developed | 118 |
| abstract_inverted_index.few-shot) | 140 |
| abstract_inverted_index.(zero-shot | 138 |
| abstract_inverted_index.Task-level | 66 |
| abstract_inverted_index.Transfer), | 67 |
| abstract_inverted_index.downstream | 87, 124 |
| abstract_inverted_index.evaluation | 51, 88, 122, 131 |
| abstract_inverted_index.facilitate | 120 |
| abstract_inverted_index.knowledge. | 109 |
| abstract_inverted_index.pioneering | 16 |
| abstract_inverted_index.(Evaluation | 62 |
| abstract_inverted_index.benchmarks. | 55 |
| abstract_inverted_index.challenging | 37 |
| abstract_inverted_index.components. | 83 |
| abstract_inverted_index.demonstrate | 24 |
| abstract_inverted_index.easy-to-use | 50 |
| abstract_inverted_index.supervision | 6 |
| abstract_inverted_index.fine-tuning). | 148 |
| abstract_inverted_index.classification | 95 |
| abstract_inverted_index.transferablity | 41 |
| abstract_inverted_index.hyper-parameter | 114 |
| abstract_inverted_index.representations | 2 |
| abstract_inverted_index.transferability | 26 |
| abstract_inverted_index.sample-efficiency | 137 |
| abstract_inverted_index.Language-augmented | 64 |
| abstract_inverted_index.language-augmented | 21, 75 |
| abstract_inverted_index.parameter-efficiency | 142 |
| abstract_inverted_index.evaluating(pre-trained) | 74 |
| abstract_inverted_index.https://computer-vision-in-the-wild.github.io/ELEVATER/ | 166 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |