Academic Journal
Training-Free Few-Shot Image Classification via Kernel Density Estimation with CLIP Embeddings.
| Τίτλος: | Training-Free Few-Shot Image Classification via Kernel Density Estimation with CLIP Embeddings. |
|---|---|
| Συγγραφείς: | Pacheco dos Santos Lima Junior, Marcos Sergio, Ortiz-de-Lazcano-Lobato, Juan Miguel, López-Rubio, Ezequiel |
| Πηγή: | Mathematics (2227-7390); Nov2025, Vol. 13 Issue 22, p3615, 28p |
| Θεματικοί όροι: | Image recognition (Computer vision), Probability density function, Machine learning, Dimensional reduction algorithms |
| Περίληψη: | Few-shot image classification aims to recognize novel classes from only a handful of labeled examples, a challenge in domains where data collection is costly or impractical. Existing solutions often rely on meta learning, fine tuning, or data augmentation, introducing computational overhead, risk of overfitting, or are not highly efficient. This paper introduces ProbaCLIP, a simple training-free approach that leverages Kernel Density Estimation (KDE) within the embedding space of Contrastive Language-Image Pre-training (CLIP). Unlike other CLIP-based methods, the proposed approach operates solely on visual embeddings and does not require text labels. Class-conditional probability densities were estimated from few-shot support examples, and queries were classified by likelihood evaluation, where Principal Component Analysis (PCA) was used for dimensionality reduction, compressing the dissimilarities between classes on each episode. We further introduced an optional bandwidth optimization strategy and a consensus decision mechanism through cross-validation, while addressing the special case of one-shot classification with distance-based measures. Extensive experiments on multiple datasets demonstrated that our method achieved competitive or superior accuracy compared to the state-of-the-art few-shot classifiers, reaching up to 98.37% accuracy in five-shot tasks and up to 99.80% in a 16-shot framework with ViT-L/14@336px. We proved our methodology by achieving high performance without gradient-based training, text supervision, or auxiliary meta-training datasets, emphasizing the effectiveness of combining pre-trained embeddings with statistical density estimation for data-scarce classification. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Training-Free Few-Shot Image Classification via Kernel Density Estimation with CLIP Embeddings. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pacheco+dos+Santos+Lima+Junior%2C+Marcos+Sergio%22">Pacheco dos Santos Lima Junior, Marcos Sergio</searchLink><br /><searchLink fieldCode="AR" term="%22Ortiz-de-Lazcano-Lobato%2C+Juan+Miguel%22">Ortiz-de-Lazcano-Lobato, Juan Miguel</searchLink><br /><searchLink fieldCode="AR" term="%22López-Rubio%2C+Ezequiel%22">López-Rubio, Ezequiel</searchLink> – Name: TitleSource Label: Source Group: Src Data: Mathematics (2227-7390); Nov2025, Vol. 13 Issue 22, p3615, 28p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+density+function%22">Probability density function</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Few-shot image classification aims to recognize novel classes from only a handful of labeled examples, a challenge in domains where data collection is costly or impractical. Existing solutions often rely on meta learning, fine tuning, or data augmentation, introducing computational overhead, risk of overfitting, or are not highly efficient. This paper introduces ProbaCLIP, a simple training-free approach that leverages Kernel Density Estimation (KDE) within the embedding space of Contrastive Language-Image Pre-training (CLIP). Unlike other CLIP-based methods, the proposed approach operates solely on visual embeddings and does not require text labels. Class-conditional probability densities were estimated from few-shot support examples, and queries were classified by likelihood evaluation, where Principal Component Analysis (PCA) was used for dimensionality reduction, compressing the dissimilarities between classes on each episode. We further introduced an optional bandwidth optimization strategy and a consensus decision mechanism through cross-validation, while addressing the special case of one-shot classification with distance-based measures. Extensive experiments on multiple datasets demonstrated that our method achieved competitive or superior accuracy compared to the state-of-the-art few-shot classifiers, reaching up to 98.37% accuracy in five-shot tasks and up to 99.80% in a 16-shot framework with ViT-L/14@336px. We proved our methodology by achieving high performance without gradient-based training, text supervision, or auxiliary meta-training datasets, emphasizing the effectiveness of combining pre-trained embeddings with statistical density estimation for data-scarce classification. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/math13223615 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 3615 Subjects: – SubjectFull: Image recognition (Computer vision) Type: general – SubjectFull: Probability density function Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Dimensional reduction algorithms Type: general Titles: – TitleFull: Training-Free Few-Shot Image Classification via Kernel Density Estimation with CLIP Embeddings. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pacheco dos Santos Lima Junior, Marcos Sergio – PersonEntity: Name: NameFull: Ortiz-de-Lazcano-Lobato, Juan Miguel – PersonEntity: Name: NameFull: López-Rubio, Ezequiel IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 22277390 Numbering: – Type: volume Value: 13 – Type: issue Value: 22 Titles: – TitleFull: Mathematics (2227-7390) Type: main |
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