关键词: 关键词: 小样本目标检测, 特征信息不足, 空间上下文, 像素关系映射

Abstract: Abstract: Enriching the semantic information available in the support sets and query sets helps the model in few-shot scenarios to understand the target categories and then effectively complete the target detection task. Therefore, a model based on spatial context and pixel relationship is proposed to address the problem of insufficient feature information for model training in few-shot object detection. The spatial context module is used to assist the pixels in constructing a local context region, obtaining the semantics of the pixels in the region for the center pixel, and enriching the image feature information. In addition, to address the problem that spatial context easily introduces noisy information, the pixel context relation module is designed to utilize the inherent feature knowledge in the image to explore the relationship between pixels, and construct intra and inter-class relation maps to correct the defect that spatial context module easily introduces noisy information. Results from extensive experiments on the PASCAL VOC datasets and the more challenging MS COCO datasets show that the three ways of segmentation utilizing the VOC datasets resulted in improvements of 2.7, 2.0, and 1.3 percentage points for the 1-shot setting where samples are extremely sparse, and an average of 0.8 percentage points for all settings with 15 evaluation metrics. The average precision (AP) improved by 0.4 and 0.6 percentage points under the COCO datasets 10shot and 30shot, respectively, and the 50% intersection over union average precision improved by 9.8 and 8.7 percentage points, respectively. Compared with other few-shot object detection methods based on meta-learning or migration learning, the proposed model shows significant detection performance improvement under different task settings, demonstrating the effectiveness of the proposed modeling approach。

Key words: Keywords: few-shot object detection, insufficient feature information, spatial context, pixel relationship mapping