Many meta-learning methods are proposed for few-shot detection. However, previous most methods have two main problems, poor detection APs, and strong bias because of imbalance datasets. Previous works mainly alleviate these issues by additional datasets, multi-relation attention mechanisms and sub-modules. However, they require more cost. In this work, for meta-learning, we find that the main challenges focus on related or irrelevant semantic features between different categories, and poor distribution of category-based meta-features. Therefore, we propose a Top-C classification loss (i.e. TCL-C) for classification task and a category-based grouping mechanism. The TCL exploits true-label and 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 make similar semantic features more compact for different categories, alleviating the strong bias problem and further improving 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 to convolve dynamically, 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. Extensive experiments on Pascal VOC dataset demonstrate that ours which combines the TCL with category-based grouping significantly outperforms previous state-of-the-art methods for few-shot detection. Compared with previous competitive baseline, ours improves detection AP by almost 4% for few-shot detection.

中文翻译：

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用于小样本检测的顶级相关元学习方法

许多元学习方法被提出用于小样本检测。然而，以前的大多数方法有两个主要问题，检测 AP 较差，以及由于数据集不平衡导致的强烈偏差。以前的工作主要通过额外的数据集、多关系注意机制和子模块来缓解这些问题。但是，它们需要更多的成本。在这项工作中，对于元学习，我们发现主要挑战集中在不同类别之间相关或不相关的语义特征，以及基于类别的元特征的分布不佳。因此，我们为分类任务提出了Top-C分类损失（即TCL-C）和基于类别的分组机制。TCL 利用真实标签和最相似的类来提高对少样本类的检测性能。根据外观和环境，基于类别的分组机制将类别分组到不同的分组中，使不同类别的相似语义特征更加紧凑，缓解强偏差问题，进一步提高检测AP。整个训练由基础模型和微调阶段组成。在训练检测模型时，将与类别相关的元特征视为权重进行动态卷积，利用组内类别之间共享分布的元特征来提高检测性能。根据分组机制，我们对元特征向量进行分组，使得组间分布差异明显，每组内的分布差异较小。在 Pascal VOC 数据集上进行的大量实验表明，我们将 TCL 与基于类别的分组相结合的方法显着优于以前的少数镜头检测方法。与之前的竞争基线相比，我们的检测 AP 提高了近 4% 的小样本检测。