Detecting objects and estimating their viewpoints in images are key tasks of
3D scene understanding. Recent approaches have achieved excellent results on
very large benchmarks for object detection and viewpoint estimation. However,
performances are still lagging behind for novel object categories with few
samples. In this paper, we tackle the problems of few-shot object detection and
few-shot viewpoint estimation. We demonstrate on both tasks the benefits of
guiding the network prediction with class-representative features extracted
from data in different modalities: image patches for object detection, and
aligned 3D models for viewpoint estimation. Despite its simplicity, our method
outperforms state-of-the-art methods by a large margin on a range of datasets,
including PASCAL and COCO for few-shot object detection, and Pascal3D+ and
ObjectNet3D for few-shot viewpoint estimation. Furthermore, when the 3D model
is not available, we introduce a simple category-agnostic viewpoint estimation
method by exploiting geometrical similarities and consistent pose labelling
across different classes. While it moderately reduces performance, this
approach still obtains better results than previous methods in this setting.
Last, for the first time, we tackle the combination of both few-shot tasks, on
three challenging benchmarks for viewpoint estimation in the wild, ObjectNet3D,
Pascal3D+ and Pix3D, showing very promising results.

本文提出了基于类代表特征和几何相似性的少样本物体检测和视角估计方法，取得了 PASCAL，COCO，Pascal3D+，ObjectNet3D 和 Pix3D 数据集中的最优结果。