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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 1: AAAI-22 Technical Tracks 1

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

February 1, 2023

Authors

Guangxing Han

Columbia University


Shiyuan Huang

Columbia University


Jiawei Ma

Columbia University


Yicheng He

Columbia University


Shih-Fu Chang

Columbia University


Proceedings:

No. 1: AAAI-22 Technical Tracks 1

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision I

Downloads:

Download PDF

Abstract:

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.

DOI:

10.1609/aaai.v36i1.19959


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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