Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment


  • Guangxing Han Columbia University
  • Shiyuan Huang Columbia University
  • Jiawei Ma Columbia University
  • Yicheng He Columbia University
  • Shih-Fu Chang Columbia University



Computer Vision (CV)


Few-shot object detection (FSOD) aims to detect objects using only a few examples. How to adapt state-of-the-art object detectors to the few-shot domain remains challenging. Object proposal is a key ingredient in modern object detectors. However, the quality of proposals generated for few-shot classes using existing methods is far worse than that of many-shot classes, e.g., missing boxes for few-shot classes due to misclassification or inaccurate spatial locations with respect to true objects. To address the noisy proposal problem, we propose a novel meta-learning based FSOD model by jointly optimizing the few-shot proposal generation and fine-grained few-shot proposal classification. To improve proposal generation for few-shot classes, we propose to learn a lightweight metric-learning based prototype matching network, instead of the conventional simple linear object/nonobject classifier, e.g., used in RPN. Our non-linear classifier with the feature fusion network could improve the discriminative prototype matching and the proposal recall for few-shot classes. To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection. Meanwhile we learn a separate Faster R-CNN detection head for many-shot base classes and show strong performance of maintaining base-classes knowledge. Our model achieves state-of-the-art performance on multiple FSOD benchmarks over most of the shots and metrics.




How to Cite

Han, G., Huang, S., Ma, J., He, Y., & Chang, S.-F. (2022). Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment. Proceedings of the AAAI Conference on Artificial Intelligence, 36(1), 780-789.



AAAI Technical Track on Computer Vision I