Top-Related Meta-Learning Method for Few-Shot Detection
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Many meta-learning methods which depend on a large amount of data and more parameters have been proposed for few-shot detection. They require more cost. However, because of imbalance of categories and less features, previous methods exist obvious problems, the strong bias and poor classification for few-shot detection. Therefore, for meta-learning method of few-shot detection, we propose a TCL which exploits the true-label example and the most similar semantics with the example, and a category-based grouping mechanism which groups categories by appearance and environment to enhance the semantic features between similar categories. The whole training consists of the base classes model and the fine-tuning phase. During training, the meta-features related to the category are regarded as the weights of the prediction layer of detection model, exploiting the meta-features with a shared distribution between categories within a group to improve the detection performance. According to group and category, we split category-related meta-features into different groups, 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 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 Especially for 1-shot detection, experiments show that our method achieves detection AP of 20
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