论文标题
使用忘记的模块进行几次分类的频道关系预测
Channel Relationship Prediction with Forget-Update Module for Few-shot Classification
论文作者
论文摘要
在本文中，我们提出了一条管道，用于推断每个类别中的每个类别的关系和使用Mosting-Update模块的查询样本的关系。我们首先提出了一个称为“通道矢量序列构建模块”的新型体系结构模块，该模块通过收集所有支持样本的总体信息和一个查询样本的总体信息，从而提高了基于序列预测模型的几杆分类方法的性能。该模块生成的通道矢量序列的组织方式是，该序列的每个时间步长包含来自所有支持样本的相应信道和要推断的查询样本的信息。通道矢量序列是通过卷积神经网络和完全连接的网络获得的，拼接的通道矢量序列是从支持样本的相应通道向量剪接的，并按原始通道顺序剪接了查询样品。另外，我们提出了一个忘记的模块，该模块由堆叠的忘记块组成。忘记块用学习的权重修改原始信息，更新块为模型建立了密集的连接。所提出的管道由通道矢量序列构建模块和忘记上升模块组成，可以在几个射击分类方案中推断出查询样本与支持样本之间的关系。实验结果表明，该管道可以在迷你胶原，CUB数据集和跨域情景上实现最新结果。
In this paper, we proposed a pipeline for inferring the relationship of each class in support set and a query sample using forget-update module. We first propose a novel architectural module called "channel vector sequence construction module", which boosts the performance of sequence-prediction-model-based few-shot classification methods by collecting the overall information of all support samples and a query sample. The channel vector sequence generated by this module is organized in a way that each time step of the sequence contains the information from the corresponding channel of all support samples and the query sample to be inferred. Channel vector sequence is obtained by a convolutional neural network and a fully connected network, and the spliced channel vector sequence is spliced of the corresponding channel vectors of support samples and a query sample in the original channel order. Also, we propose a forget-update module consisting of stacked forget-update blocks. The forget block modify the original information with the learned weights and the update block establishes a dense connection for the model. The proposed pipeline, which consists of channel vector sequence construction module and forget-update module, can infer the relationship between the query sample and support samples in few-shot classification scenario. Experimental results show that the pipeline can achieve state-of-the-art results on miniImagenet, CUB dataset, and cross-domain scenario.
