Dynamic Relevance Learning for Few-Shot Object Detection

• URL: http://arxiv.org/abs/2108.02235v3
• Date: Wed, 22 Mar 2023 13:26:59 GMT
• Title: Dynamic Relevance Learning for Few-Shot Object Detection
• Authors: Weijie Liu, Chong Wang, Haohe Li, Shenghao Yu and Jiafei Wu
• Abstract summary: We propose a dynamic relevance learning model, which utilizes the relationship between all support images and Region of Interest (RoI) on the query images to construct a dynamic graph convolutional network (GCN) The proposed model achieves the best overall performance, which shows its effectiveness of learning more generalized features.
• Score: 6.550840743803705
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Expensive bounding-box annotations have limited the development of object detection task. Thus, it is necessary to focus on more challenging task of few-shot object detection. It requires the detector to recognize objects of novel classes with only a few training samples. Nowadays, many existing popular methods adopting training way similar to meta-learning have achieved promising performance, such as Meta R-CNN series. However, support data is only used as the class attention to guide the detecting of query images each time. Their relevance to each other remains unexploited. Moreover, a lot of recent works treat the support data and query images as independent branch without considering the relationship between them. To address this issue, we propose a dynamic relevance learning model, which utilizes the relationship between all support images and Region of Interest (RoI) on the query images to construct a dynamic graph convolutional network (GCN). By adjusting the prediction distribution of the base detector using the output of this GCN, the proposed model serves as a hard auxiliary classification task, which guides the detector to improve the class representation implicitly. Comprehensive experiments have been conducted on Pascal VOC and MS-COCO dataset. The proposed model achieves the best overall performance, which shows its effectiveness of learning more generalized features. Our code is available at https://github.com/liuweijie19980216/DRL-for-FSOD.

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