Detecting objects and estimating their viewpoint in images are key tasks of 3D scene understanding. Recent approaches have achieved excellent results on very large benchmarks for object detection and view- point 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 pro- pose a meta-learning framework that can be applied to both tasks, possi- bly including 3D data. Our models improve the results on objects of novel classes by leveraging on rich feature information originating from base classes with many samples. A simple joint feature embedding module is proposed to make the most of this feature sharing. Despite its simplicity, our method outperforms state-of-the-art methods by a large margin on a range of datasets, including PASCAL VOC and MS COCO for few-shot object detection, and Pascal3D+ and ObjectNet3D for few-shot view- point estimation. And for the first time, we tackle the combination of both few-shot tasks, on ObjectNet3D, showing promising results.


Short video (90 s) Long video (9 mins)


Task-Relevant Class Data

Few-Shot Object Detection

Few-Shot Viewpoint Estimation


Paper, Code and Citation

To cite our paper,

    author    = {Yang Xiao and Renaud Marlet},
    title     = {Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild},
    booktitle = {European Conference on Computer Vision (ECCV)},
    year      = {2020}}


We thank Vincent Lepetit and Yuming Du for helpful discussions.