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 https://irvlutd.github.io/FewSOL.

翻译：我们采用了微小对象学习(FewSOL)数据集,用每个物体的几张图像进行对象识别。我们从不同角度从每个物体中捕获了336个真实世界物体,每个物体有9 RGB-D图像。提供了对象分割面罩、对象构成和对象属性;此外,使用330 3D对象模型生成的合成图像用于增强数据集。我们调查了(一) 微小物体分类,(二) 利用我们的数据集,与最新技术方法联合进行对象分割和微小碎片分类,以便进行微小的学习和元学习。评价结果显示,在机器人环境中,微小物体分类仍有很大的空间有待改进。我们的数据集可用于研究一组微小物体识别问题,如分类、探测和分解、形状重建、形状估计、键点通信和属性识别。数据集和代码可在https://irvlutd.github.io/FewSOL查阅。