VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning Article Swipe
Wenjia Xu
,
Yongqin Xian
,
Jiuniu Wang
,
Bernt Schiele
,
Zeynep Akata
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.10444
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.10444
Human-annotated attributes serve as powerful semantic embeddings in zero-shot learning. However, their annotation process is labor-intensive and needs expert supervision. Current unsupervised semantic embeddings, i.e., word embeddings, enable knowledge transfer between classes. However, word embeddings do not always reflect visual similarities and result in inferior zero-shot performance. We propose to discover semantic embeddings containing discriminative visual properties for zero-shot learning, without requiring any human annotation. Our model visually divides a set of images from seen classes into clusters of local image regions according to their visual similarity, and further imposes their class discrimination and semantic relatedness. To associate these clusters with previously unseen classes, we use external knowledge, e.g., word embeddings and propose a novel class relation discovery module. Through quantitative and qualitative evaluation, we demonstrate that our model discovers semantic embeddings that model the visual properties of both seen and unseen classes. Furthermore, we demonstrate on three benchmarks that our visually-grounded semantic embeddings further improve performance over word embeddings across various ZSL models by a large margin.
Related Topics
Concepts
Computer science
Natural language processing
Word (group theory)
Artificial intelligence
Discriminative model
Annotation
Set (abstract data type)
Class (philosophy)
Similarity (geometry)
Margin (machine learning)
Zero (linguistics)
Semantic similarity
Process (computing)
Image (mathematics)
Shot (pellet)
Information retrieval
Machine learning
Mathematics
Linguistics
Operating system
Programming language
Geometry
Organic chemistry
Chemistry
Philosophy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2203.10444
- https://arxiv.org/pdf/2203.10444
- OA Status
- green
- Cited By
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221167378
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4221167378Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2203.10444Digital Object Identifier
- Title
-
VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-20Full publication date if available
- Authors
-
Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep AkataList of authors in order
- Landing page
-
https://arxiv.org/abs/2203.10444Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2203.10444Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2203.10444Direct OA link when available
- Concepts
-
Computer science, Natural language processing, Word (group theory), Artificial intelligence, Discriminative model, Annotation, Set (abstract data type), Class (philosophy), Similarity (geometry), Margin (machine learning), Zero (linguistics), Semantic similarity, Process (computing), Image (mathematics), Shot (pellet), Information retrieval, Machine learning, Mathematics, Linguistics, Operating system, Programming language, Geometry, Organic chemistry, Chemistry, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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