It is widely accepted that reasoning about object shape is important for
object recognition. However, the most powerful object recognition methods today
do not explicitly make use of object shape during learning. In this work,
motivated by recent developments in low-shot learning, findings in
developmental psychology, and the increased use of synthetic data in computer
vision research, we investigate how reasoning about 3D shape can be used to
improve low-shot learning methods' generalization performance. We propose a new
way to improve existing low-shot learning approaches by learning a
discriminative embedding space using 3D object shape, and using this embedding
by learning how to map images into it. Our new approach improves the
performance of image-only low-shot learning approaches on multiple datasets. We
also introduce Toys4K, a 3D object dataset with the largest number of object
categories currently available, which supports low-shot learning.

该研究提出了一种新的方法，通过学习 3D 物体形状来改进现有的低样本学习方法，并在多个数据集上证明了其有效性，同时介绍了最大种类数量的 3D 对象数据集 Toys4K。