Top-Related Meta-Learning Method for Few-Shot Detection

Many meta-learning methods are proposed for few-shot detection. However, previous most methods have two main problems, strong bias between all classes, and poor classification for few-shot classes. Previous works mainly depend on additional datasets and sub-module to alleviate these issues. However, they require more cost. In this paper, we find that the main challenge lies on imbalance between the examples, and poor shared distribution of class-based meta-features. Therefore, we propose a TCL for classification task and a category-based grouping mechanism. The TCL exploits the classification score of true-label class and the classification score of the most similar class to improve detection performance on few-shot classes. According to appearance and environment, the category-based grouping mechanism groups categories into different groupings to promote different similar semantic features more compact, alleviating the strong bias problem and further improving few-shot detection APs. The whole training consists of the base model and the fine-tuning phase. During training detection model, the category-related meta-features are regarded as the weights of the detection layer, exploiting the meta-features with a shared distribution between categories within a group to improve the detection performance. According to grouping mechanism, we group the meta-features vectors, so that the distribution difference between groups is obvious, and the one within each group is less. Experimental results on Pascal VOC dataset demonstrate that ours which combines the TCL with category-based grouping significantly outperforms previous state-of-the-art methods for 1, 2-shot detection, and obtains detection APs of almost 30% for 3-shot detection.