Adaptive Cross-Modal Few-Shot Learning
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Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. However, leveraging cross-modal information in a few-shot setting has yet to be explored. When the support from visual information is limited in few-shot image classification, semantic representatins (learned from unsupervised text corpora) can provide strong prior knowledge and context to help learning. Based on this intuition, we design a model that is able to leverage visual and semantic features in the context of few-shot classification. We propose an adaptive mechanism that is able to effectively combine both modalities conditioned on categories. Through a series of experiments, we show that our method boosts the performance of metric-based approaches by effectively exploiting language structure. Using this extra modality, our model bypass current unimodal state-of-the-art methods by a large margin on two important benchmarks: mini-ImageNet and tiered-ImageNet. The improvement in performance is particularly large when the number of shots are small.
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