Feature fine-tuning and attribute representation transformation for zero-shot learning Article Swipe
Shanmin Pang
,
Xin He
,
Wenyu Hao
,
Yang Long
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.cviu.2023.103811
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.cviu.2023.103811
Zero-Shot Learning (ZSL) aims to generalize a pretrained classification model to unseen classes with the help of auxiliary semantic information. Recent generative methods are based on the paradigm of synthesizing unseen visual data from class attributes. A mapping is learnt from semantic attributes to visual features extracted by a pre-trained backbone such as ResNet101 by training a generative adversarial network. Considering the domain-shift problem between pre-trained backbone and task ZSL dataset as well as the information asymmetry problem between images and attributes, this manuscript suggests that the visual-semantic balance should be learnt separately from the ZSL models. In particular, we propose a plug-and-play Attribute Representation Transformation (ART) framework to pre-process visual features with a contrastive regression module and an attribute place-holder module. Our contrastive regression loss is a tailored design for visual-attribute transformation, which gains favorable properties from both classification and regression losses. As for the attribute place-holder module, an end-to-end mapping loss function is introduced to build the relationship between transformed features and semantic attributes. Experiments conducted on five popular benchmarks manifest that the proposed ART framework can significantly benefit existing generative models in both ZSL and generalized ZSL settings.
Related Topics
Concepts
Computer science
Artificial intelligence
Transformation (genetics)
Generative grammar
Feature (linguistics)
Representation (politics)
Pattern recognition (psychology)
Generative model
Machine learning
Margin (machine learning)
Semantics (computer science)
Regression
Process (computing)
Semantic feature
Natural language processing
Mathematics
Operating system
Philosophy
Law
Chemistry
Statistics
Gene
Linguistics
Biochemistry
Political science
Programming language
Politics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.cviu.2023.103811
- OA Status
- gold
- Cited By
- 3
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386052195
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386052195Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.cviu.2023.103811Digital Object Identifier
- Title
-
Feature fine-tuning and attribute representation transformation for zero-shot learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-22Full publication date if available
- Authors
-
Shanmin Pang, Xin He, Wenyu Hao, Yang LongList of authors in order
- Landing page
-
https://doi.org/10.1016/j.cviu.2023.103811Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://durham-repository.worktribe.com/output/1815174Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Transformation (genetics), Generative grammar, Feature (linguistics), Representation (politics), Pattern recognition (psychology), Generative model, Machine learning, Margin (machine learning), Semantics (computer science), Regression, Process (computing), Semantic feature, Natural language processing, Mathematics, Operating system, Philosophy, Law, Chemistry, Statistics, Gene, Linguistics, Biochemistry, Political science, Programming language, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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