Few-shot learning is an extremely challenging task in computer vision that has attracted increased research attention in recent years. However, most recent methods do not fully use the task’s information, and few of the seen samples result in large intraclass differences among the same classes. In this paper, we propose a novel task encoding with distribution calibration (TEDC) model for few-shot learning, which uses the relationships among the feature distributions to reduce intraclass differences. In the TEDC model, an integrated feature extraction module (IFEM) is proposed, which extracts the multiangle visual features of an image and fuses them to obtain more representative features. To effectively utilize the task information, a novel task encoding module (TEM) is proposed, which obtains the task features by fusing all the seen samples’ information and uses them to adjust all the samples’ features for more generalizable task-specific representations. We also propose a distribution calibration module (DCM) to reduce the bias between the distribution of the support features and the query features in the same class. Extensive experiments show that our proposed TEDC model achieves an excellent performance and outperforms the state-of-the-art methods on three widely used few-shot classification benchmarks, specifically miniImageNet, tieredImageNet and CUB-200-2011.

中文翻译：

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具有分布校准的任务编码，用于少样本学习


小样本学习是计算机视觉中一项极具挑战性的任务，近年来引起了越来越多的研究关注。然而，最近的方法并没有充分利用任务的信息，并且很少有看到的样本会导致同一类之间存在较大的类内差异。在本文中，我们提出了一种用于小样本学习的新颖的带有分布校准的任务编码（TEDC）模型，该模型利用特征分布之间的关系来减少类内差异。在TEDC模型中，提出了集成特征提取模块（IFEM），提取图像的多角度视觉特征并将其融合以获得更具代表性的特征。为了有效地利用任务信息，提出了一种新颖的任务编码模块（TEM），它通过融合所有看到的样本信息来获得任务特征，并使用它们来调整所有样本的特征以获得更通用的特定于任务的表示。我们还提出了一个分布校准模块（DCM）来减少同一类中支持特征和查询特征的分布之间的偏差。大量实验表明，我们提出的 TEDC 模型在三个广泛使用的少样本分类基准（特别是 miniImageNet、tieredImageNet 和 CUB-200-2011）上实现了出色的性能，并且优于最先进的方法。