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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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Self-supervised monocular depth estimation has been widely investigated to estimate depth images and relative poses from RGB images. This framework is attractive for researchers because the depth and pose networks can be trained from just time sequence images without the need for the ground truth depth and poses. In this work, we estimate the depth around a robot (360 degree view) using time sequence spherical camera images, from a camera whose parameters are unknown. We propose a learnable axisymmetric camera model which accepts distorted spherical camera images with two fisheye camera images. In addition, we trained our models with a photo-realistic simulator to generate ground truth depth images to provide supervision. Moreover, we introduced loss functions to provide floor constraints to reduce artifacts that can result from reflective floor surfaces. We demonstrate the efficacy of our method using the spherical camera images from the GO Stanford dataset and pinhole camera images from the KITTI dataset to compare our method's performance with that of baseline method in learning the camera parameters.
