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Attentional Feature Fusion for Few-Shot Learning | IEEE Conference Publication | IEEE Xplore

Attentional Feature Fusion for Few-Shot Learning

Publisher: IEEE

Abstract:

Few-shot Learning (FSL) approaches aim to develop generalizable models that can classify novel data points with a small set of labeled training data for each class. FSL a...View more

Abstract:

Few-shot Learning (FSL) approaches aim to develop generalizable models that can classify novel data points with a small set of labeled training data for each class. FSL approaches have the potential to narrow down the performance gap between machines and humans, but it is challenging because humans can quickly adapt to new activities and make decisions based on organized and reusable concepts. However, existing FSL approaches learn complex feature representations ignoring the conceptual information. We propose an Attentional Feature Fusion for Few-shot Learning (AF3), a semi-supervised approach that combines features of multiple scales and utilizes prior knowledge to learn better human interpretable concepts. Attentional feature fusion involves merging features from various layers and branches through an attention mechanism that prioritizes different features through attentional weights. AF3 extracts more discriminative features by generating attention maps for both query and support images. We evaluated our AF3 model in FSL settings on three benchmark datasets, including a fine-grained image classification. Extensive experiments show that our AF3 model outperformed the state-of-the-art in the most challenging 5-way-5-shot learning tasks.
Date of Conference: 30 June 2024 - 05 July 2024
Date Added to IEEE Xplore: 09 September 2024
ISBN Information:

ISSN Information:

Publisher: IEEE
Conference Location: Yokohama, Japan

References

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