We introduce the Few-Shot Object Learning (FewSOL) dataset for object
recognition with a few images per object. We captured 336 real-world objects
with 9 RGB-D images per object from different views. Object segmentation masks,
object poses and object attributes are provided. In addition, synthetic images
generated using 330 3D object models are used to augment the dataset. We
investigated (i) few-shot object classification and (ii) joint object
segmentation and few-shot classification with the state-of-the-art methods for
few-shot learning and meta-learning using our dataset. The evaluation results
show that there is still a large margin to be improved for few-shot object
classification in robotic environments. Our dataset can be used to study a set
of few-shot object recognition problems such as classification, detection and
segmentation, shape reconstruction, pose estimation, keypoint correspondences
and attribute recognition. The dataset and code are available at
this https URL.

介绍了 Few-Shot Object Learning (FewSOL) 数据集，用于针对每个物体进行少量图像识别，研究结果表明目前机器人环境下还有一定提升空间，适用于研究分类、检测、分割、形状重构、姿态估计、关键点对应和属性识别等少量图像识别问题。