TY - JOUR N2 - Few-shot image classification is a challenging task that aims to learn from a limited number of labelled training images a classification model that can be generalised to unseen classes. Two strategies are usually taken to improve the classification performances of few-shot image classifiers: either applying data augmentation to enlarge the sample size of the training set and reduce overfitting, or involving attention mechanisms to highlight discriminative spatial regions or channels. However, naively applying them to few-shot classifiers directly and separately may lead to undesirable results; for example, some augmented images may focus majorly on the background rather than the object, which brings additional noises to the training process. In this paper, we propose a unified framework, the selectively augmented attention (SAA) network, that carefully integrates the best of the two approaches in an end-to-end fashion via a selective best match module to select the most representative images from the augmented training set. The selected images tend to concentrate on the objects with less irrelevant background, which can assist the subsequent calculation of attentions by alleviating the interference from background. Moreover, we design a joint attention module to jointly learn both the spatial and channel-wise attentions. Experimental results on four benchmark datasets showcase the superior classification performance of the proposed SAA network compared with the state-of-the-arts. KW - Few-shot image classification KW - Data augmentation KW - Attention mechanism KW - Metric-based methods A1 - Li, Xiaoxu A1 - Wang, Xiangyang A1 - Zhu, Rui A1 - Ma, Zhanyu A1 - Cao, Jie A1 - Xue, Jing-Hao UR - https://doi.org/10.1109/TCSVT.2024.3480279 SN - 1051-8215 AV - public N1 - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions. ID - discovery10198870 Y1 - 2024/10/14/ PB - Institute of Electrical and Electronics Engineers (IEEE) TI - Selectively augmented attention network for few-shot image classification JF - IEEE Transactions on Circuits and Systems for Video Technology ER -