PLIP: Language-Image Pre-training for Person Representation Learning Article Swipe
Jialong Zuo
,
Changqian Yu
,
Nong Sang
,
Changxin Gao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.08386
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.08386
Language-image pre-training is an effective technique for learning powerful representations in general domains. However, when directly turning to person representation learning, these general pre-training methods suffer from unsatisfactory performance. The reason is that they neglect critical person-related characteristics, i.e., fine-grained attributes and identities. To address this issue, we propose a novel language-image pre-training framework for person representation learning, termed PLIP. Specifically, we elaborately design three pretext tasks: 1) Text-guided Image Colorization, aims to establish the correspondence between the person-related image regions and the fine-grained color-part textual phrases. 2) Image-guided Attributes Prediction, aims to mine fine-grained attribute information of the person body in the image; and 3) Identity-based Vision-Language Contrast, aims to correlate the cross-modal representations at the identity level rather than the instance level. Moreover, to implement our pre-train framework, we construct a large-scale person dataset with image-text pairs named SYNTH-PEDES by automatically generating textual annotations. We pre-train PLIP on SYNTH-PEDES and evaluate our models by spanning downstream person-centric tasks. PLIP not only significantly improves existing methods on all these tasks, but also shows great ability in the zero-shot and domain generalization settings. The code, dataset and weights will be released at~\url{https://github.com/Zplusdragon/PLIP}
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Concepts
Closed captioning
Computer science
Discriminative model
Artificial intelligence
Representation (politics)
Code (set theory)
Generalization
Matching (statistics)
Image (mathematics)
Natural language processing
Feature learning
Machine learning
Set (abstract data type)
Law
Political science
Mathematical analysis
Mathematics
Politics
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Statistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.08386
- https://arxiv.org/pdf/2305.08386
- OA Status
- green
- Cited By
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376653864
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4376653864Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.08386Digital Object Identifier
- Title
-
PLIP: Language-Image Pre-training for Person Representation LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-15Full publication date if available
- Authors
-
Jialong Zuo, Changqian Yu, Nong Sang, Changxin GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.08386Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2305.08386Direct 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/2305.08386Direct OA link when available
- Concepts
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Closed captioning, Computer science, Discriminative model, Artificial intelligence, Representation (politics), Code (set theory), Generalization, Matching (statistics), Image (mathematics), Natural language processing, Feature learning, Machine learning, Set (abstract data type), Law, Political science, Mathematical analysis, Mathematics, Politics, Programming language, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 14, 2023: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.tasks: | 66 |
| abstract_inverted_index.termed | 58 |
| abstract_inverted_index.ability | 175 |
| abstract_inverted_index.address | 44 |
| abstract_inverted_index.between | 76 |
| abstract_inverted_index.dataset | 135, 185 |
| abstract_inverted_index.general | 11, 22 |
| abstract_inverted_index.methods | 24, 166 |
| abstract_inverted_index.neglect | 34 |
| abstract_inverted_index.pretext | 65 |
| abstract_inverted_index.propose | 48 |
| abstract_inverted_index.regions | 80 |
| abstract_inverted_index.textual | 85, 144 |
| abstract_inverted_index.turning | 16 |
| abstract_inverted_index.weights | 187 |
| abstract_inverted_index.However, | 13 |
| abstract_inverted_index.critical | 35 |
| abstract_inverted_index.directly | 15 |
| abstract_inverted_index.domains. | 12 |
| abstract_inverted_index.evaluate | 152 |
| abstract_inverted_index.existing | 165 |
| abstract_inverted_index.identity | 117 |
| abstract_inverted_index.improves | 164 |
| abstract_inverted_index.instance | 122 |
| abstract_inverted_index.learning | 7 |
| abstract_inverted_index.phrases. | 86 |
| abstract_inverted_index.powerful | 8 |
| abstract_inverted_index.released | 190 |
| abstract_inverted_index.spanning | 156 |
| abstract_inverted_index.Contrast, | 108 |
| abstract_inverted_index.Moreover, | 124 |
| abstract_inverted_index.attribute | 95 |
| abstract_inverted_index.construct | 131 |
| abstract_inverted_index.correlate | 111 |
| abstract_inverted_index.effective | 4 |
| abstract_inverted_index.establish | 73 |
| abstract_inverted_index.framework | 53 |
| abstract_inverted_index.implement | 126 |
| abstract_inverted_index.learning, | 20, 57 |
| abstract_inverted_index.pre-train | 128, 147 |
| abstract_inverted_index.settings. | 182 |
| abstract_inverted_index.technique | 5 |
| abstract_inverted_index.zero-shot | 178 |
| abstract_inverted_index.Attributes | 89 |
| abstract_inverted_index.attributes | 40 |
| abstract_inverted_index.color-part | 84 |
| abstract_inverted_index.downstream | 157 |
| abstract_inverted_index.framework, | 129 |
| abstract_inverted_index.generating | 143 |
| abstract_inverted_index.image-text | 137 |
| abstract_inverted_index.Prediction, | 90 |
| abstract_inverted_index.SYNTH-PEDES | 140, 150 |
| abstract_inverted_index.Text-guided | 68 |
| abstract_inverted_index.cross-modal | 113 |
| abstract_inverted_index.elaborately | 62 |
| abstract_inverted_index.identities. | 42 |
| abstract_inverted_index.information | 96 |
| abstract_inverted_index.large-scale | 133 |
| abstract_inverted_index.Image-guided | 88 |
| abstract_inverted_index.annotations. | 145 |
| abstract_inverted_index.fine-grained | 39, 83, 94 |
| abstract_inverted_index.performance. | 28 |
| abstract_inverted_index.pre-training | 1, 23, 52 |
| abstract_inverted_index.Colorization, | 70 |
| abstract_inverted_index.Specifically, | 60 |
| abstract_inverted_index.automatically | 142 |
| abstract_inverted_index.significantly | 163 |
| abstract_inverted_index.Identity-based | 106 |
| abstract_inverted_index.Language-image | 0 |
| abstract_inverted_index.correspondence | 75 |
| abstract_inverted_index.generalization | 181 |
| abstract_inverted_index.language-image | 51 |
| abstract_inverted_index.person-centric | 158 |
| abstract_inverted_index.person-related | 36, 78 |
| abstract_inverted_index.representation | 19, 56 |
| abstract_inverted_index.unsatisfactory | 27 |
| abstract_inverted_index.Vision-Language | 107 |
| abstract_inverted_index.representations | 9, 114 |
| abstract_inverted_index.characteristics, | 37 |
| abstract_inverted_index.at~\url{https://github.com/Zplusdragon/PLIP} | 191 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile |