The goal of few-shot learning is to classify unseen categories with few labeled samples. Recently, the low-level information metric-learning based methods have achieved satisfying performance, since local representations (LRs) are more consistent between seen and unseen classes. However, most of these methods deal with each category in the support set independently, which is not sufficient to measure the relation between features, especially in a certain task. Moreover, the low-level information-based metric learning method suffers when dominant objects of different scales exist in a complex background. To address these issues, this paper proposes a novel Multi-scale Adaptive Task Attention Network (MATANet) for few-shot learning. Specifically, we first use a multi-scale feature generator to generate multiple features at different scales. Then, an adaptive task attention module is proposed to select the most important LRs among the entire task. Afterwards, a similarity-to-class module and a fusion layer are utilized to calculate a joint multi-scale similarity between the query image and the support set. Extensive experiments on popular benchmarks clearly show the effectiveness of the proposed MATANet compared with state-of-the-art methods.
翻译:短片学习的目的是用很少标签的样本来分类隐蔽类别。最近,低层次的信息衡量学习方法取得了令人满意的业绩,因为当地代表(LRS)在可见和看不见的类别之间更加一致。然而,这些方法大多是独立地处理支助组的每个类别,这不足以衡量特征之间的关系,特别是在某一任务中。此外,低层次的信息基础衡量学习方法在复杂的背景中存在不同尺度的主要对象时会受到影响。为解决这些问题,本文件提议建立一个新的多尺度适应性任务关注网络(MATANet),供少片学习使用。具体地说,我们首先使用多尺度的特性生成器在不同尺度上产生多种特征。然后,提出一个适应性任务关注模块,以便在整个任务中选择最重要的 LTDs。随后,使用一个类似的类模块和一个聚合层来计算查询图像与支助组之间的多尺度联合相似性。关于普及基准的广泛实验清楚地表明了拟议的MATANet相对于最新方法的有效性。