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

• URL: http://arxiv.org/abs/2104.07719v1
• Date: Thu, 15 Apr 2021 19:01:27 GMT
• Title: Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment
• Authors: Guangxing Han, Shiyuan Huang, Jiawei Ma, Yicheng He, Shih-Fu Chang
• Abstract summary: Few-shot object detection (FSOD) aims to detect objects using only few examples. We propose a meta-learning based few-shot object detection method by transferring meta-knowledge learned from data-abundant base classes to data-scarce novel classes.
• Score: 33.446875089255876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot object detection (FSOD) aims to detect objects using only few examples. It's critically needed for many practical applications but so far remains challenging. We propose a meta-learning based few-shot object detection method by transferring meta-knowledge learned from data-abundant base classes to data-scarce novel classes. Our method incorporates a coarse-to-fine approach into the proposal based object detection framework and integrates prototype based classifiers into both the proposal generation and classification stages. To improve proposal generation for few-shot novel classes, we propose to learn a lightweight matching network to measure the similarity between each spatial position in the query image feature map and spatially-pooled class features, instead of the traditional object/nonobject classifier, thus generating category-specific proposals and improving proposal recall for novel classes. To address the spatial misalignment between generated proposals and few-shot class examples, we propose a novel attentive feature alignment method, thus improving the performance of few-shot object detection. Meanwhile we jointly learn a Faster R-CNN detection head for base classes. Extensive experiments conducted on multiple FSOD benchmarks show our proposed approach achieves state of the art results under (incremental) few-shot learning settings.

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