Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks

• URL: http://arxiv.org/abs/2112.09791v1
• Date: Fri, 17 Dec 2021 22:08:15 GMT
• Title: Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks
• Authors: Guangxing Han, Yicheng He, Shiyuan Huang, Jiawei Ma, Shih-Fu Chang
• Abstract summary: Few-shot object detection (FSOD) aims to detect never-seen objects using few examples. We propose a novel FSOD model using heterogeneous graph convolutional networks.
• Score: 33.446875089255876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot object detection (FSOD) aims to detect never-seen objects using few examples. This field sees recent improvement owing to the meta-learning techniques by learning how to match between the query image and few-shot class examples, such that the learned model can generalize to few-shot novel classes. However, currently, most of the meta-learning-based methods perform pairwise matching between query image regions (usually proposals) and novel classes separately, therefore failing to take into account multiple relationships among them. In this paper, we propose a novel FSOD model using heterogeneous graph convolutional networks. Through efficient message passing among all the proposal and class nodes with three different types of edges, we could obtain context-aware proposal features and query-adaptive, multiclass-enhanced prototype representations for each class, which could help promote the pairwise matching and improve final FSOD accuracy. Extensive experimental results show that our proposed model, denoted as QA-FewDet, outperforms the current state-of-the-art approaches on the PASCAL VOC and MSCOCO FSOD benchmarks under different shots and evaluation metrics.

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