Multi-domain few-shot image recognition with knowledge transfer
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Jun 28, 2021 · Few-shot image recognition aims to recognize novel categories with only few labeled images in each class. Existing metric-based and ...
Metric-learning methods try to learn an appropriate feature embedding s- pace in which images of the same category are similar while images of different ...
Missing: domain | Show results with:domain
Few-shot image recognition aims to recognize novel categories with only few labeled images in each class. Existing metric-based and meta-based few-shot ...
A methodology for the estimation of the severity of wheat Fusarium head blight (FHB) with a small sample dataset based on transfer learning technology and ...
Such knowledge is then used by a generator to augment the sparse training data to help the downstream classification tasks. Extensive experiments show that our ...
A novel Knowledge Transfer Network architecture (KTN) for few-shot image recognition that jointly incorporates visual feature learning, knowledge inferring ...
In this work, we investigate an underexplored issue in FSIG, dubbed as incompatible knowledge transfer, which would significantly degrade the realisticness of ...
Missing: Multi- | Show results with:Multi-
We showcase our new meta-dataset by performing an experimental evaluation for several use cases, including transfer learning, few-shot meta-learning, and cross- ...
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Conventional few-shot learning (FSL) mainly focuses on knowledge transfer from a single source dataset to a recognition scenario with only a few training ...
This paper considers few-shot image classification under the cross-domain scenario, where the train-to-test domain gap compromises classification accuracy.
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