Multimodal Prototypical Networks for Few-shot Learning Article Swipe
Frederik Pahde
,
Mihai Puscas
,
Tassilo Klein
,
Moin Nabi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/wacv48630.2021.00269
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/wacv48630.2021.00269
Although providing exceptional results for many computer vision tasks, state-of-the-art deep learning algorithms catastrophically struggle in low data scenarios. However, if data in additional modalities exist (e.g. text) this can compensate for the lack of data and improve the classification results. To overcome this data scarcity, we design a cross-modal feature generation framework capable of enriching the low populated embedding space in few-shot scenarios, leveraging data from the auxiliary modality. Specifically, we train a generative model that maps text data into the visual feature space to obtain more reliable prototypes. This allows to exploit data from additional modalities (e.g. text) during training while the ultimate task at test time remains classification with exclusively visual data. We show that in such cases nearest neighbor classification is a viable approach and outperform state-of-the-art single-modal and multimodal few-shot learning methods on the CUB-200 and Oxford-102 datasets.
Related Topics
Concepts
Computer science
Artificial intelligence
Exploit
Modalities
Feature (linguistics)
Modality (human–computer interaction)
Embedding
Machine learning
Feature vector
Generative grammar
Task (project management)
Multimodal learning
Deep learning
Space (punctuation)
Modal
k-nearest neighbors algorithm
Engineering
Philosophy
Polymer chemistry
Operating system
Chemistry
Sociology
Systems engineering
Computer security
Social science
Linguistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/wacv48630.2021.00269
- OA Status
- green
- Cited By
- 7
- References
- 72
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3103390191
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3103390191Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/wacv48630.2021.00269Digital Object Identifier
- Title
-
Multimodal Prototypical Networks for Few-shot LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Frederik Pahde, Mihai Puscas, Tassilo Klein, Moin NabiList of authors in order
- Landing page
-
https://doi.org/10.1109/wacv48630.2021.00269Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2011.08899Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Exploit, Modalities, Feature (linguistics), Modality (human–computer interaction), Embedding, Machine learning, Feature vector, Generative grammar, Task (project management), Multimodal learning, Deep learning, Space (punctuation), Modal, k-nearest neighbors algorithm, Engineering, Philosophy, Polymer chemistry, Operating system, Chemistry, Sociology, Systems engineering, Computer security, Social science, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2024: 4, 2023: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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72Number of works referenced by this work
- Related works (count)
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20Other works algorithmically related by OpenAlex
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