Abstract:The main challenge of few-shot learning lies in the limited labeled sample of data. In addition, since image-level labels are usually not accurate in describing the features of images, it leads to difficulty for the model to have good generalization ability and robustness. This problem has not been well solved yet, and existing metric-based methods still have room for improvement. To address this issue, we propose a few-shot learning method based on a three-dimension attention mechanism and self-supervised lea… Show more
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