GCN-based Semantic Relation Network for Few-Shot Object Detection
Jaegi Hwang, Seongju Kang, Kwangsue Chung · 2022 13th International Conference on Information and Communication Technology Convergence (ICTC) · 2022
Few-shot object detection has arisen to address the issue of data scarcity due to the long-tail distribution problem. The goal of few-shot object detection is to allow a pre-trained detector to generalize novel objects with few-shot data samples. However, few-shot object detectors misclassify novel objects into pre-trained base classes frequently. Existing methods applied a large margin classifier to address this problem, but they do not work well in data scarcity settings. In this paper, we propose graph convolution network-based semantic relation network to capture the semantic relations for few-shot object detection. Regardless of the amount of data, the semantic relations among object classes remain constant, which can support the classification of novel objects. Semantic relations are trained by the attention-based module adaptively to training data. Graph convolution network updates the object classifier using semantic relations. The object classifier is integrated and trained with the visual features. Through the comparison experiments with Two-stage Fine-tuning Approach (TFA), we observe that our proposed method achieves higher performance on few-shot settings than TFA.