Multi-scale Adaptive Task Attention Network for Few-Shot Learning
This work provides an incremental improvement for researchers and practitioners working on few-shot image classification, particularly in scenarios with complex backgrounds and multi-scale objects.
This paper addresses the challenge of few-shot learning, particularly when dealing with varying object scales and inter-category relationships. The proposed Multi-scale Adaptive Task Attention Network (MATANet) generates multi-scale features and uses an adaptive task attention module to select important local representations, leading to improved performance on popular benchmarks.
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.