Language Models as Zero-shot Visual Semantic Learners Article Swipe
Yue Jiao
,
Jonathon Hare
,
Adam Prügel‐Bennett
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2107.12021
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2107.12021
Visual Semantic Embedding (VSE) models, which map images into a rich semantic embedding space, have been a milestone in object recognition and zero-shot learning. Current approaches to VSE heavily rely on static word em-bedding techniques. In this work, we propose a Visual Se-mantic Embedding Probe (VSEP) designed to probe the semantic information of contextualized word embeddings in visual semantic understanding tasks. We show that the knowledge encoded in transformer language models can be exploited for tasks requiring visual semantic understanding.The VSEP with contextual representations can distinguish word-level object representations in complicated scenes as a compositional zero-shot learner. We further introduce a zero-shot setting with VSEPs to evaluate a model's ability to associate a novel word with a novel visual category. We find that contextual representations in language mod-els outperform static word embeddings, when the compositional chain of object is short. We notice that current visual semantic embedding models lack a mutual exclusivity bias which limits their performance.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2107.12021
- https://arxiv.org/pdf/2107.12021
- OA Status
- green
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3183645852
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- OpenAlex ID
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https://openalex.org/W3183645852Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2107.12021Digital Object Identifier
- Title
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Language Models as Zero-shot Visual Semantic LearnersWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-07-26Full publication date if available
- Authors
-
Yue Jiao, Jonathon Hare, Adam Prügel‐BennettList of authors in order
- Landing page
-
https://arxiv.org/abs/2107.12021Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2107.12021Direct 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/2107.12021Direct OA link when available
- Concepts
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Computer science, Natural language processing, Artificial intelligence, Embedding, Word (group theory), Semantic memory, Visual Objects, Language model, Object (grammar), Speech recognition, Linguistics, Psychology, Cognition, Perception, Philosophy, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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41Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.VSEPs | 104 |
| abstract_inverted_index.chain | 135 |
| abstract_inverted_index.novel | 113, 117 |
| abstract_inverted_index.probe | 48 |
| abstract_inverted_index.tasks | 75 |
| abstract_inverted_index.their | 155 |
| abstract_inverted_index.which | 5, 153 |
| abstract_inverted_index.work, | 37 |
| abstract_inverted_index.(VSEP) | 45 |
| abstract_inverted_index.Visual | 0, 41 |
| abstract_inverted_index.images | 7 |
| abstract_inverted_index.limits | 154 |
| abstract_inverted_index.models | 70, 147 |
| abstract_inverted_index.mutual | 150 |
| abstract_inverted_index.notice | 141 |
| abstract_inverted_index.object | 19, 87, 137 |
| abstract_inverted_index.scenes | 91 |
| abstract_inverted_index.short. | 139 |
| abstract_inverted_index.space, | 13 |
| abstract_inverted_index.static | 31, 129 |
| abstract_inverted_index.tasks. | 60 |
| abstract_inverted_index.visual | 57, 77, 118, 144 |
| abstract_inverted_index.Current | 24 |
| abstract_inverted_index.ability | 109 |
| abstract_inverted_index.current | 143 |
| abstract_inverted_index.encoded | 66 |
| abstract_inverted_index.further | 98 |
| abstract_inverted_index.heavily | 28 |
| abstract_inverted_index.mod-els | 127 |
| abstract_inverted_index.model's | 108 |
| abstract_inverted_index.models, | 4 |
| abstract_inverted_index.propose | 39 |
| abstract_inverted_index.setting | 102 |
| abstract_inverted_index.Semantic | 1 |
| abstract_inverted_index.designed | 46 |
| abstract_inverted_index.evaluate | 106 |
| abstract_inverted_index.language | 69, 126 |
| abstract_inverted_index.learner. | 96 |
| abstract_inverted_index.semantic | 11, 50, 58, 78, 145 |
| abstract_inverted_index.Embedding | 2, 43 |
| abstract_inverted_index.Se-mantic | 42 |
| abstract_inverted_index.associate | 111 |
| abstract_inverted_index.category. | 119 |
| abstract_inverted_index.embedding | 12, 146 |
| abstract_inverted_index.exploited | 73 |
| abstract_inverted_index.introduce | 99 |
| abstract_inverted_index.knowledge | 65 |
| abstract_inverted_index.learning. | 23 |
| abstract_inverted_index.milestone | 17 |
| abstract_inverted_index.requiring | 76 |
| abstract_inverted_index.zero-shot | 22, 95, 101 |
| abstract_inverted_index.approaches | 25 |
| abstract_inverted_index.contextual | 82, 123 |
| abstract_inverted_index.em-bedding | 33 |
| abstract_inverted_index.embeddings | 55 |
| abstract_inverted_index.outperform | 128 |
| abstract_inverted_index.word-level | 86 |
| abstract_inverted_index.complicated | 90 |
| abstract_inverted_index.distinguish | 85 |
| abstract_inverted_index.embeddings, | 131 |
| abstract_inverted_index.exclusivity | 151 |
| abstract_inverted_index.information | 51 |
| abstract_inverted_index.recognition | 20 |
| abstract_inverted_index.techniques. | 34 |
| abstract_inverted_index.transformer | 68 |
| abstract_inverted_index.performance. | 156 |
| abstract_inverted_index.compositional | 94, 134 |
| abstract_inverted_index.understanding | 59 |
| abstract_inverted_index.contextualized | 53 |
| abstract_inverted_index.representations | 83, 88, 124 |
| abstract_inverted_index.understanding.The | 79 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |