Nov 5, 2021 · In this paper, we propose Feature Disentanglement and Hallucination Network (FDH-Net), which jointly performs feature disentanglement and hallucination for FSL ...

Nov 11, 2021 · The results confirm that our framework performs favorably against state-of-the-art metric-learning and hallucination-based FSL models. Index ...

In this paper, we propose Feature Disentanglement and Hallucination Network (FDH-Net), which jointly performs feature disentanglement and hallucination for FSL ...

In this paper, we propose Feature Disentanglement and Hallucination Network (FDH-Net), which jointly performs feature disentanglement and hallucination for FSL ...

Joint feature disentanglement and hallucination for few-shot image classification. CC Lin, HL Chu, YCF Wang, CL Lei. IEEE Transactions on Image Processing 30, ...

Dec 7, 2024 · ... (Joint Feature Disentanglement and Hallucination for Few-shot Image Classification). ... Few-shot learning (FSL) refers to the learning task ...

[22] employed feature disentanglement to generate augmented features through hallucination, aiming to mitigate data sparsity in few-shot classification.

Aug 1, 2023 · Few-shot classification is a challenging task of computer vision and is critical to the data-sparse scenario like rare disease diagnosis.

Aug 2, 2023 · A feature disentanglement and hallucination network [8] is trained to disentangle features into categorical-specific and appearance-specific ...

Feature augmentation is a straightforward way to alleviate the data-sparse issue in few-shot classification. However, mimicking the original feature ...