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少样本数据集泛化是研究良好的少样本分类问题的一种具有挑战性的变体，其中给出了多个数据集的不同训练集，目的是训练一个可适应的模型，然后可以通过仅使用几个例子从新数据集学习类。为此，我们提出利用不同的训练集来构建一个通用模板:通过插入适当的组件，可以定义广泛的数据集专用模型的部分模型。因此，对于每个新的几杆分类问题，我们的方法只需要推断少量参数插入到通用模板中。我们设计了一个单独的网络，为每个给定的任务生成这些参数的初始化，然后我们通过梯度下降的几个步骤来微调其提出的初始化。与以前的方法相比，我们的方法参数效率更高，可扩展性更强，适应性更强，并在具有挑战性的Meta-Dataset基准测试上达到了最好的性能。
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Learning a Universal Template for Few-shot Dataset Generalization
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Zero-shot action recognition is the task of classifying action categories that are not available in the training set. In this setting, the standard evaluation protocol is to use existing action recognition datasets (e.g. UCF101) and randomly split the classes into seen and unseen. However, most recent work builds on representations pre-trained on the Kinetics dataset, where classes largely overlap with classes in the zero-shot evaluation datasets. As a result, classes which are supposed to be unseen, are present during supervised pre-training, invalidating the condition of the zero-shot setting. A similar concern was previously noted several years ago for image based zero-shot recognition, but has not been considered by the zero-shot action recognition community. In this paper, we propose a new split for true zero-shot action recognition with no overlap between unseen test classes and training or pre-training classes. We benchmark several recent approaches on the proposed True Zero-Shot (TruZe) Split for UCF101 and HMDB51, with zero-shot and generalized zero-shot evaluation. In our extensive analysis we find that our TruZe splits are significantly harder than comparable random splits as nothing is leaking from pre-training, i.e. unseen performance is consistently lower, up to 9.4% for zero-shot action recognition. In an additional evaluation we also find that similar issues exist in the splits used in few-shot action recognition, here we see differences of up to 14.1%. We publish our splits and hope that our benchmark analysis will change how the field is evaluating zero- and few-shot action recognition moving forward.
